General Setup

General Setup


Create a new analysis directories.

- general directory

- for plots

- for output of summary results

- for baseline tables

- for genetic analyses

- for Cox regression results
source("scripts/functions.R")
source("scripts/pack02.packages.R")

* General packages...

* Genomic packages...

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

source("scripts/colors.R")

Background

This notebook contains additional figures of the project.

Loading data

# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20230614.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))

Fix some variables

We need to get the ‘conventional unit’ versions of cholesterols.

AERNASE.clin <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

Additional figures

Age and sex

We want to create per-age-group figures median ± interquartile range.

  • Box and Whisker plot for PCSK9 plaque levels by sex.
  • Box and Whisker plot for PCSK9 plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).

# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ Gender,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("Gender"),
                  y = "PCSK9", 
                  xlab = "gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Gender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

library(dplyr)

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin$AgeGroup, AERNASE.clin$Gender)
       
        female male
  <55       11   27
  55-64     43  124
  65-74     58  189
  75-84     38  119
  85+        4    9
table(AERNASE.clin$AgeGroupSex)

   <55 females      <55 males 55-64 females     55-64 males  65-74 females    65-74 males  75-84 females    75-84 males    85+ females      85+ males 
            11             27             43            124             58            189             38            119              4              9 

Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.


# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ AgeGroup,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "PCSK9", 
                  xlab = "Age groups (years)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "PCSK9", 
                  xlab = "Age groups (years) per gender",
                  ylab = "PCSK9 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup_perGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Hypertension & blood pressure

We want to create figures of PCSK9 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.

  • Box and Whisker plot for PCSK9 plaque levels by hypertension group (no, yes)
  • Box and Whisker plot for PCSK9 plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 
mutate: new variable 'SBPGroup' (factor) with 5 unique values and 16% NA
table(AERNASE.clin$SBPGroup, AERNASE.clin$Gender)
         
          female male
  <120         7   22
  120-139     30   81
  140-159     36  120
  160+        62  167

Now we can draw some graphs of plaque PCSK9 levels per sex and hypertension/blood pressure group as median ± interquartile range.

PCSK9

detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)
compare_means(PCSK9 ~ SBPGroup, data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
filter: removed 97 rows (16%), 525 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
filter: removed 97 rows (16%), 525 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypertension.selfreport, data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
filter: removed 13 rows (2%), 609 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
filter: removed 13 rows (2%), 609 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypertension.drugs, data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "PCSK9", 
                  xlab = "Hypertension medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.HypertensionDrugs.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
filter: removed 97 rows (16%), 525 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 97 rows (16%), 525 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
filter: removed 13 rows (2%), 609 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 13 rows (2%), 609 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "PCSK9", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed 98 rows (16%), 524 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
filter: removed 98 rows (16%), 524 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
filter: removed 14 rows (2%), 608 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
filter: removed 14 rows (2%), 608 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Hypercholesterolemia & LDL levels

We want to create figures of PCSK9 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.

  • Box and Whisker plot for PCSK9 plaque levels by hypercholesterolemia (risk614) group (no, yes)
  • Box and Whisker plot for PCSK9 plaque levels by lipid-lowering drugs group (no, yes)
  • Box and Whisker plot for PCSK9 plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 
mutate: new variable 'LDLGroup' (factor) with 6 unique values and 38% NA
table(AERNASE.clin$LDLGroup, AERNASE.clin$Gender)
         
          female male
  <100        45  141
  100-129     25   73
  130-159     18   42
  160-189      9   20
  190+         2    9
require(sjlabelled)

AERNASE.clin$risk614 <- to_factor(AERNASE.clin$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin)
AERNASE.clin[,"Hypercholesterolemia"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin)

table(AERNASE.clin$risk614, AERNASE.clin$Hypercholesterolemia)
   
     no yes
  0 190   0
  1   0 404
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

Now we can draw some graphs of plaque PCSK9 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

PCSK9


compare_means(PCSK9 ~ LDLGroup, data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
filter: removed 238 rows (38%), 384 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 238 rows (38%), 384 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
filter: removed 238 rows (38%), 384 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 238 rows (38%), 384 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypercholesterolemia, data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
filter: removed 28 rows (5%), 594 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 28 rows (5%), 594 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
filter: removed 28 rows (5%), 594 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 28 rows (5%), 594 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Med.Statin.LLD, data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "PCSK9", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "PCSK9", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed one row (<1%), 621 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed 239 rows (38%), 383 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
filter: removed 239 rows (38%), 383 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
filter: removed 29 rows (5%), 593 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
filter: removed 29 rows (5%), 593 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Kidney function (eGFR)

We want to create figures of PCSK9 levels stratified by kidney function.

  • Box and Whisker plot for PCSK9 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
  • Box and Whisker plot for PCSK9 plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))
mutate: new variable 'eGFRGroup' (factor) with 5 unique values and 4% NA
table(AERNASE.clin$eGFRGroup, AERNASE.clin$Gender)
       
        female male
  15-29      2    6
  30-59     38  102
  60-89     85  251
  90+       25   88
table(AERNASE.clin$eGFRGroup, AERNASE.clin$KDOQI)
       
        No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
  15-29                         0                      0            0                0              8               0
  30-59                         0                      0            0              140              0               0
  60-89                         0                      0          336                0              0               0
  90+                           0                    113            0                0              0               0

Now we can draw some graphs of plaque PCSK9 levels per sex and kidney function group as median ± interquartile range.

PCSK9


# Global test

compare_means(PCSK9 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
filter: removed 25 rows (4%), 597 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
filter: removed 25 rows (4%), 597 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
filter: removed 25 rows (4%), 597 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 25 rows (4%), 597 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_byGender.pdf"), plot = last_plot())
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compare_means(PCSK9 ~ KDOQI, data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
filter: removed 8 rows (1%), 614 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "PCSK9", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
filter: removed 8 rows (1%), 614 rows remaining
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
filter: removed 8 rows (1%), 614 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "PCSK9", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 8 rows (1%), 614 rows remaining
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI_byGender.pdf"), plot = last_plot())
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compare_means(PCSK9 ~ eGFRGroup,  data = AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
filter: removed 25 rows (4%), 597 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "PCSK9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
filter: removed 25 rows (4%), 597 rows remaining
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_KDOQI.pdf"), plot = last_plot())
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BMI

We want to create figures of PCSK9 levels stratified by BMI.

  • Box and Whisker plot for PCSK9 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
  • Box and Whisker plot for PCSK9 plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 
mutate: new variable 'BMIGroup' (factor) with 6 unique values and 5% NA
# require(labelled)
# AERNASE.clin$BMI_US <- as_factor(AERNASE.clin$BMI_US)
# AERNASE.clin$BMI_WHO <- as_factor(AERNASE.clin$BMI_WHO)
# table(AERNASE.clin$BMI_WHO, AERNASE.clin$BMI_US)

table(AERNASE.clin$BMIGroup, AERNASE.clin$Gender)
         
          female male
  <18.5        3    2
  18.5-24     46  161
  25-29       69  220
  30-35       18   54
  35+          6   12
table(AERNASE.clin$BMIGroup, AERNASE.clin$BMI_WHO)
         
          No data available/missing Underweight Normal Overweight Obese
  <18.5                           0           5      0          0     0
  18.5-24                         0           0    207          0     0
  25-29                           0           0      0        288     0
  30-35                           0           0      0          0    72
  35+                             0           0      0          0    18

Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.

PCSK9


# Global test
compare_means(PCSK9 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
filter: removed 31 rows (5%), 591 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 31 rows (5%), 591 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
filter: removed 31 rows (5%), 591 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 31 rows (5%), 591 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byGender.pdf"), plot = last_plot())
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compare_means(PCSK9 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
filter: removed 32 rows (5%), 590 rows remaining
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "PCSK9 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
filter: removed 32 rows (5%), 590 rows remaining
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byWHO.pdf"), plot = last_plot())
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Diabetes

We want to create figures of PCSK9 levels stratified by type 2 diabetes.

  • Box and Whisker plot for PCSK9 plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.

PCSK9


compare_means(PCSK9 ~ DiabetesStatus,  
              data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
filter: no rows removed
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "PCSK9",
                  xlab = "Diabetes status",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: no rows removed
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes.pdf"), plot = last_plot())
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compare_means(PCSK9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
filter: no rows removed
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "PCSK9",
                  xlab = "Diabetes status per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: no rows removed
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes_byGender.pdf"), plot = last_plot())
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NA
NA

Smoking

We want to create figures of PCSK9 levels stratified by smoking.

  • Box and Whisker plot for PCSK9 plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.

PCSK9


# Global test
compare_means(PCSK9 ~ SmokerStatus,  data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
filter: removed 23 rows (4%), 599 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "PCSK9", 
                  xlab = "Smoker status",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 23 rows (4%), 599 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking.pdf"), plot = last_plot())
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compare_means(PCSK9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
filter: removed 23 rows (4%), 599 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "PCSK9", 
                  xlab = "Smoker status per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 23 rows (4%), 599 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking_byGender.pdf"), plot = last_plot())
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Stenosis

We want to create figures of PCSK9 levels stratified by stenosis grade.

  • Box and Whisker plot for PCSK9 plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))
mutate: new variable 'StenoticGroup' (factor) with 4 unique values and 4% NA
table(AERNASE.clin$StenoticGroup, AERNASE.clin$Gender)
       
        female male
  <70        6   34
  70-89     72  198
  90+       70  220
table(AERNASE.clin$stenose, AERNASE.clin$StenoticGroup)
                  
                   <70 70-89 90+
  missing            0     0   0
  0-49%              2     0   0
  50-70%            38     0   0
  70-90%             0   270   0
  90-99%             0     0 284
  100% (Occlusion)   0     0   5
  NA                 0     0   0
  50-99%             0     0   1
  70-99%             0     0   0
  99                 0     0   0

Now we can draw some graphs of plaque PCSK9 levels per sex and age group as median ± interquartile range.

PCSK9


# Global test
compare_means(PCSK9 ~ StenoticGroup,  data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
filter: removed 22 rows (4%), 600 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "PCSK9", 
                  xlab = "Stenotic grade",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
filter: removed 22 rows (4%), 600 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis.pdf"), plot = last_plot())
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compare_means(PCSK9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
filter: removed 22 rows (4%), 600 rows remaining
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "PCSK9", 
                  xlab = "Stenotic grade per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
filter: removed 22 rows (4%), 600 rows remaining
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis_byGender.pdf"), plot = last_plot())
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Symptoms

We want to create per-symptom figures.

library(dplyr)

table(AERNASE.clin$AgeGroup, AERNASE.clin$AsymptSympt2G)
       
        Asymptomatic Symptomatic
  <55             10          28
  55-64           24         143
  65-74           30         217
  75-84           15         141
  85+              1          12
table(AERNASE.clin$Gender, AERNASE.clin$AsymptSympt2G)
        
         Asymptomatic Symptomatic
  female           15         138
  male             65         403
table(AERNASE.clin$AsymptSympt2G)

Asymptomatic  Symptomatic 
          80          541 

Now we can draw some graphs of plaque PCSK9 levels per symptom group as median ± interquartile range.

PCSK9


# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "PCSK9",
                  title = "PCSK9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "PCSK9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.pdf"), plot = last_plot())
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rm(p1)

compare_means(PCSK9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "PCSK9",
                  title = "PCSK9 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())
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rm(p1)

Forest plots

We would also like to visualize the multivariable analyses results.

library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target
NA

Forest plots.

PCSK9

dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="PCSK9"]),
                  low = c(models_target$low95CI[models_target$Predictor=="PCSK9"]),
                  high = c(models_target$up95CI[models_target$Predictor=="PCSK9"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque PCSK9 normalized expression (1 SD increment, n = 622)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.forest.pdf"), plot = fp)
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rm(fp)

Target expression vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the PCSK9 plaque levels. These include:

  • IL2
  • IL4
  • IL5
  • IL6
  • IL8
  • IL9
  • IL10
  • IL12
  • IL13
  • IL21
  • INFG
  • TNFA
  • MIF
  • MCP1
  • MIP1a
  • RANTES
  • MIG
  • IP10
  • Eotaxin1
  • TARC
  • PARC
  • MDC
  • OPG
  • sICAM1
  • VEGFA
  • TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay.

  • MMP2
  • MMP8
  • MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the PCSK9` plaque levels.

We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.

Prepare data

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin)[names(AERNASE.clin) == "IL6"] <- "IL6rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin <- merge(AERNASE.clin, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin <- AERNASE.clin %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))Warning: `mutate_each_()` was deprecated in dplyr 0.7.0.
Please use `across()` instead.Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(proteins_of_interest)

# Now:
data %>% select(all_of(proteins_of_interest))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
  
for(PROTEIN in 1:length(proteins_of_interest)){

  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AERNASE.clin <-  AERNASE.clin %>% mutate(AERNASE.clin[,var.temp] == replace(AERNASE.clin[,var.temp], AERNASE.clin[,var.temp]==0, NA))

  AERNASE.clin[,var.temp][AERNASE.clin[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AERNASE.clin[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AERNASE.clin[,var.temp])),").\n"))
  
  AERNASE.clin <- AERNASE.clin %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AERNASE.clin[,var.temp] - mean(AERNASE.clin[,var.temp], na.rm = TRUE))/sd(AERNASE.clin[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AERNASE.clin[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AERNASE.clin[,var.temp.rank] <- NULL
  names(AERNASE.clin)[names(AERNASE.clin) == "RANK"] <- var.temp.rank
}

Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.292605, n = 182).
Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(var.temp)

# Now:
data %>% select(all_of(var.temp))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.mutate_at: new variable 'RANK' (double) with 183 unique values and 71% NA
* renaming RANK to IL2_rank.

Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.263666, n = 164).
mutate_at: new variable 'RANK' (double) with 165 unique values and 74% NA
* renaming RANK to IL4_rank.

Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.287781, n = 179).
mutate_at: new variable 'RANK' (double) with 180 unique values and 71% NA
* renaming RANK to IL5_rank.

Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.303859, n = 189).
mutate_at: new variable 'RANK' (double) with 190 unique values and 70% NA
* renaming RANK to IL6_rank.

Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.289389, n = 180).
mutate_at: new variable 'RANK' (double) with 181 unique values and 71% NA
* renaming RANK to IL8_rank.

Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.337621, n = 210).
mutate_at: new variable 'RANK' (double) with 211 unique values and 66% NA
* renaming RANK to IL9_rank.

Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.252412, n = 157).
mutate_at: new variable 'RANK' (double) with 158 unique values and 75% NA
* renaming RANK to IL10_rank.

Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.266881, n = 166).
mutate_at: new variable 'RANK' (double) with 167 unique values and 73% NA
* renaming RANK to IL12_rank.

Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to IL13_rank.

Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to IL21_rank.

Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.278135, n = 173).
mutate_at: new variable 'RANK' (double) with 174 unique values and 72% NA
* renaming RANK to INFG_rank.

Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.263666, n = 164).
mutate_at: new variable 'RANK' (double) with 165 unique values and 74% NA
* renaming RANK to TNFA_rank.

Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to MIF_rank.

Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.366559, n = 228).
mutate_at: new variable 'RANK' (double) with 229 unique values and 63% NA
* renaming RANK to MCP1_rank.

Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.344051, n = 214).
mutate_at: new variable 'RANK' (double) with 215 unique values and 66% NA
* renaming RANK to MIP1a_rank.

Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.363344, n = 226).
mutate_at: new variable 'RANK' (double) with 227 unique values and 64% NA
* renaming RANK to RANTES_rank.

Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.364952, n = 227).
mutate_at: new variable 'RANK' (double) with 228 unique values and 64% NA
* renaming RANK to MIG_rank.

Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.332797, n = 207).
mutate_at: new variable 'RANK' (double) with 208 unique values and 67% NA
* renaming RANK to IP10_rank.

Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to Eotaxin1_rank.

Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.326367, n = 203).
mutate_at: new variable 'RANK' (double) with 204 unique values and 67% NA
* renaming RANK to TARC_rank.

Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to PARC_rank.

Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.345659, n = 215).
mutate_at: new variable 'RANK' (double) with 216 unique values and 65% NA
* renaming RANK to MDC_rank.

Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to OPG_rank.

Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.369775, n = 230).
mutate_at: new variable 'RANK' (double) with 231 unique values and 63% NA
* renaming RANK to sICAM1_rank.

Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.323151, n = 201).
mutate_at: new variable 'RANK' (double) with 202 unique values and 68% NA
* renaming RANK to VEGFA_rank.

Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.371383, n = 231).
mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA
* renaming RANK to TGFB_rank.

Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.371383, n = 231).
mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA
* renaming RANK to MMP2_rank.

Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.371383, n = 231).
mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA
* renaming RANK to MMP8_rank.

Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.371383, n = 231).
mutate_at: new variable 'RANK' (double) with 232 unique values and 63% NA
* renaming RANK to MMP9_rank.
# rm(var.temp, var.temp.rank)

Visualize transformations

We will just visualize these transformations.

proteins_of_interest_rank_target <- c("PCSK9", proteins_of_interest_rank)

proteins_of_interest_target <- c("PCSK9", proteins_of_interest)

for(PROTEIN_GENE in proteins_of_interest_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}
Plotting protein PCSK9.
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.

for(PROTEIN_GENE in proteins_of_interest_rank_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
Plotting protein PCSK9.
Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.

NA

Correlations

Here we calculate correlations between PCSK9 and 28 other cytokines. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:

  • L: LUMINEX
  • E: ELISA
  • a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages.auto("devtools")
devtools::install_github("kassambara/ggcorrplot")
Using github PAT from envvar GITHUB_PAT
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (0a85456d) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin, 
                          select = c(proteins_of_interest_rank_target)
                                    )

# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)

corr_biomarkers.rank <- round(cor(matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_target <- c("PCSK9 (RNA)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
Warning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with ties
# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 16,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

rm(p1)

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of PCSK9` with other cytokines only.

PCSK9

temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
[1] 2.763428
p1 <- ggpubr::ggbarplot(temp, 
                x = "CytokineY", 
                y = "SpearmanRho",
                fill = "CytokineY",               # change fill color by cyl
                # color = "white",            # Set bar border colors to white
                # palette = uithof_color,            # jco journal color palett. see ?ggpar
                xlab = "Cytokine",
                sort.val = "desc",          # Sort the value in dscending order
                sort.by.groups = FALSE,     # Don't sort inside each group
                x.text.angle = 45, # Rotate vertically x axis texts
                cex = 1.25
                )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(paste("Spearman's"~italic(rho))))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

rm(p1)

Another version - probably not good.

temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
[1] 2.763428
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           # palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           # ylab = expression(log[10]~"("~italic(p)~")-value"),
           # ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 4, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(log[10]~"("~italic(p)~")-value"))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

rm(temp, p1)

Target expression vs. cytokines plaque levels lm()

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque PCSK9 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of CXCL10.

- processing IL2_rank
Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(PROTEIN)

# Now:
data %>% select(all_of(PROTEIN))

See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -20.593910      0.001813  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2587 -1.9577 -1.0634  0.0277 30.4990 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.891e+01  1.185e+01  -1.596   0.1124  
currentDF[, TRAIT]  1.127e-01  3.289e-01   0.343   0.7322  
Age                -2.803e-02  3.970e-02  -0.706   0.4811  
Gendermale          7.476e-01  7.702e-01   0.971   0.3330  
ORdate_epoch        1.784e-03  9.375e-04   1.903   0.0586 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.346 on 177 degrees of freedom
Multiple R-squared:  0.02891,   Adjusted R-squared:  0.006963 
F-statistic: 1.317 on 4 and 177 DF,  p-value: 0.2653

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL2_rank 
Effect size...............: 0.112729 
Standard error............: 0.328888 
Odds ratio (effect size)..: 1.119 
Lower 95% CI..............: 0.587 
Upper 95% CI..............: 2.133 
T-value...................: 0.342756 
P-value...................: 0.7321886 
R^2.......................: 0.028908 
Adjusted r^2..............: 0.006963 
Sample size of AE DB......: 622 
Sample size of model......: 182 
Missing data %............: 70.73955 

- processing IL4_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -27.527084      0.002387  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5858 -2.1639 -1.0114  0.0978 30.2270 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -25.195774  13.932271  -1.808   0.0724 .
currentDF[, TRAIT]   0.013329   0.377219   0.035   0.9719  
Age                 -0.025919   0.045149  -0.574   0.5667  
Gendermale           0.928555   0.885623   1.048   0.2960  
ORdate_epoch         0.002283   0.001106   2.065   0.0406 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.675 on 159 degrees of freedom
Multiple R-squared:  0.03722,   Adjusted R-squared:  0.01299 
F-statistic: 1.536 on 4 and 159 DF,  p-value: 0.1941

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL4_rank 
Effect size...............: 0.013329 
Standard error............: 0.377219 
Odds ratio (effect size)..: 1.013 
Lower 95% CI..............: 0.484 
Upper 95% CI..............: 2.123 
T-value...................: 0.035334 
P-value...................: 0.971858 
R^2.......................: 0.037215 
Adjusted r^2..............: 0.012994 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL5_rank
filter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -24.929820      0.002172  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5362 -2.1010 -1.0818  0.0518 30.2894 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -23.113669  12.789076  -1.807   0.0724 .
currentDF[, TRAIT]   0.066669   0.348968   0.191   0.8487  
Age                 -0.026370   0.042169  -0.625   0.5326  
Gendermale           0.987344   0.819466   1.205   0.2299  
ORdate_epoch         0.002108   0.001025   2.057   0.0412 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.553 on 174 degrees of freedom
Multiple R-squared:  0.03529,   Adjusted R-squared:  0.01312 
F-statistic: 1.591 on 4 and 174 DF,  p-value: 0.1787

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL5_rank 
Effect size...............: 0.066669 
Standard error............: 0.348968 
Odds ratio (effect size)..: 1.069 
Lower 95% CI..............: 0.539 
Upper 95% CI..............: 2.118 
T-value...................: 0.191045 
P-value...................: 0.8487128 
R^2.......................: 0.035292 
Adjusted r^2..............: 0.013115 
Sample size of AE DB......: 622 
Sample size of model......: 179 
Missing data %............: 71.22186 

- processing IL6_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -17.243264      0.001542  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2515 -1.9304 -1.1086 -0.0916 30.5808 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.644e+01  1.116e+01  -1.473   0.1425  
currentDF[, TRAIT]  9.355e-03  3.329e-01   0.028   0.9776  
Age                -1.641e-02  4.031e-02  -0.407   0.6844  
Gendermale          8.476e-01  7.512e-01   1.128   0.2607  
ORdate_epoch        1.516e-03  8.908e-04   1.702   0.0905 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.452 on 184 degrees of freedom
Multiple R-squared:  0.02396,   Adjusted R-squared:  0.002742 
F-statistic: 1.129 on 4 and 184 DF,  p-value: 0.3441

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL6_rank 
Effect size...............: 0.009355 
Standard error............: 0.332899 
Odds ratio (effect size)..: 1.009 
Lower 95% CI..............: 0.526 
Upper 95% CI..............: 1.938 
T-value...................: 0.0281 
P-value...................: 0.9776125 
R^2.......................: 0.02396 
Adjusted r^2..............: 0.002742 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing IL8_rank
filter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.594343      0.001404  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-3.166 -1.843 -1.106  0.005 30.667 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.299e+01  1.130e+01  -1.150    0.252
currentDF[, TRAIT]  1.272e-01  3.372e-01   0.377    0.706
Age                -3.365e-02  3.858e-02  -0.872    0.384
Gendermale          4.626e-01  7.677e-01   0.603    0.548
ORdate_epoch        1.349e-03  8.979e-04   1.502    0.135

Residual standard error: 4.323 on 175 degrees of freedom
Multiple R-squared:  0.0221,    Adjusted R-squared:  -0.0002564 
F-statistic: 0.9885 on 4 and 175 DF,  p-value: 0.4152

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL8_rank 
Effect size...............: 0.127242 
Standard error............: 0.337158 
Odds ratio (effect size)..: 1.136 
Lower 95% CI..............: 0.586 
Upper 95% CI..............: 2.199 
T-value...................: 0.377395 
P-value...................: 0.7063371 
R^2.......................: 0.022096 
Adjusted r^2..............: -0.000256 
Sample size of AE DB......: 622 
Sample size of model......: 180 
Missing data %............: 71.06109 

- processing IL9_rank
filter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.168996      0.001293  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.1193 -1.9856 -1.2093  0.1884 30.4120 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.331e+01  9.930e+00  -1.341   0.1815  
currentDF[, TRAIT]  3.405e-01  3.054e-01   1.115   0.2662  
Age                -3.870e-02  3.768e-02  -1.027   0.3056  
Gendermale          6.756e-01  7.021e-01   0.962   0.3370  
ORdate_epoch        1.392e-03  7.602e-04   1.831   0.0685 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.382 on 205 degrees of freedom
Multiple R-squared:  0.03046,   Adjusted R-squared:  0.01154 
F-statistic:  1.61 on 4 and 205 DF,  p-value: 0.1731

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL9_rank 
Effect size...............: 0.340451 
Standard error............: 0.305389 
Odds ratio (effect size)..: 1.406 
Lower 95% CI..............: 0.773 
Upper 95% CI..............: 2.557 
T-value...................: 1.114812 
P-value...................: 0.2662361 
R^2.......................: 0.030458 
Adjusted r^2..............: 0.01154 
Sample size of AE DB......: 622 
Sample size of model......: 210 
Missing data %............: 66.23794 

- processing IL10_rank
filter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -22.793267      0.001995  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8876 -1.9775 -1.1854  0.0065 30.1392 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -23.377853  14.266930  -1.639   0.1034  
currentDF[, TRAIT]   0.411129   0.375707   1.094   0.2756  
Age                 -0.019509   0.044705  -0.436   0.6632  
Gendermale           0.880991   0.866269   1.017   0.3108  
ORdate_epoch         0.002095   0.001120   1.871   0.0633 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.514 on 152 degrees of freedom
Multiple R-squared:  0.03512,   Adjusted R-squared:  0.009728 
F-statistic: 1.383 on 4 and 152 DF,  p-value: 0.2424

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL10_rank 
Effect size...............: 0.411129 
Standard error............: 0.375707 
Odds ratio (effect size)..: 1.509 
Lower 95% CI..............: 0.722 
Upper 95% CI..............: 3.15 
T-value...................: 1.094281 
P-value...................: 0.2755628 
R^2.......................: 0.03512 
Adjusted r^2..............: 0.009728 
Sample size of AE DB......: 622 
Sample size of model......: 157 
Missing data %............: 74.75884 

- processing IL12_rank
filter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -25.814559      0.002247  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6741 -2.1470 -1.0721  0.0146 30.1903 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -23.656542  13.933331  -1.698   0.0915 .
currentDF[, TRAIT]   0.103256   0.372317   0.277   0.7819  
Age                 -0.026438   0.045110  -0.586   0.5587  
Gendermale           0.925473   0.868952   1.065   0.2884  
ORdate_epoch         0.002161   0.001103   1.959   0.0518 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.66 on 161 degrees of freedom
Multiple R-squared:  0.03474,   Adjusted R-squared:  0.01076 
F-statistic: 1.449 on 4 and 161 DF,  p-value: 0.2204

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL12_rank 
Effect size...............: 0.103256 
Standard error............: 0.372317 
Odds ratio (effect size)..: 1.109 
Lower 95% CI..............: 0.534 
Upper 95% CI..............: 2.3 
T-value...................: 0.277334 
P-value...................: 0.7818795 
R^2.......................: 0.034741 
Adjusted r^2..............: 0.01076 
Sample size of AE DB......: 622 
Sample size of model......: 166 
Missing data %............: 73.3119 

- processing IL13_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.519110      0.001321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0146 -2.0196 -1.2092  0.1909 30.4644 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.304e+01  9.733e+00  -1.340   0.1816  
currentDF[, TRAIT]  3.245e-01  2.924e-01   1.110   0.2683  
Age                -2.700e-02  3.562e-02  -0.758   0.4492  
Gendermale          6.793e-01  6.698e-01   1.014   0.3116  
ORdate_epoch        1.309e-03  7.513e-04   1.742   0.0828 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.369 on 225 degrees of freedom
Multiple R-squared:  0.02751,   Adjusted R-squared:  0.01022 
F-statistic: 1.591 on 4 and 225 DF,  p-value: 0.1775

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL13_rank 
Effect size...............: 0.324491 
Standard error............: 0.292429 
Odds ratio (effect size)..: 1.383 
Lower 95% CI..............: 0.78 
Upper 95% CI..............: 2.454 
T-value...................: 1.109641 
P-value...................: 0.2683382 
R^2.......................: 0.027513 
Adjusted r^2..............: 0.010224 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing IL21_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.519110      0.001321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6627 -2.0106 -1.2234  0.0735 30.5088 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.342e+01  9.745e+00  -1.377   0.1698  
currentDF[, TRAIT]  1.698e-01  2.920e-01   0.582   0.5615  
Age                -2.977e-02  3.559e-02  -0.836   0.4038  
Gendermale          6.966e-01  6.719e-01   1.037   0.3009  
ORdate_epoch        1.353e-03  7.515e-04   1.800   0.0732 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.378 on 225 degrees of freedom
Multiple R-squared:  0.02366,   Adjusted R-squared:  0.006301 
F-statistic: 1.363 on 4 and 225 DF,  p-value: 0.2477

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL21_rank 
Effect size...............: 0.16982 
Standard error............: 0.292023 
Odds ratio (effect size)..: 1.185 
Lower 95% CI..............: 0.669 
Upper 95% CI..............: 2.101 
T-value...................: 0.58153 
P-value...................: 0.5614655 
R^2.......................: 0.023658 
Adjusted r^2..............: 0.006301 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing INFG_rank
filter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -22.830645      0.002005  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.2760 -2.0839 -1.1621  0.0481 29.9023 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -26.341077  13.025803  -2.022   0.0447 *
currentDF[, TRAIT]   0.477658   0.375693   1.271   0.2053  
Age                 -0.018453   0.043075  -0.428   0.6689  
Gendermale           1.063952   0.858060   1.240   0.2167  
ORdate_epoch         0.002321   0.001025   2.265   0.0248 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.557 on 168 degrees of freedom
Multiple R-squared:  0.04099,   Adjusted R-squared:  0.01816 
F-statistic: 1.795 on 4 and 168 DF,  p-value: 0.1321

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: INFG_rank 
Effect size...............: 0.477658 
Standard error............: 0.375693 
Odds ratio (effect size)..: 1.612 
Lower 95% CI..............: 0.772 
Upper 95% CI..............: 3.367 
T-value...................: 1.271405 
P-value...................: 0.2053419 
R^2.......................: 0.040992 
Adjusted r^2..............: 0.018158 
Sample size of AE DB......: 622 
Sample size of model......: 173 
Missing data %............: 72.18649 

- processing TNFA_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -21.953259      0.001931  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5656 -2.0484 -1.2071 -0.1664 30.2991 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -21.026177  13.900491  -1.513   0.1324  
currentDF[, TRAIT]   0.161735   0.371858   0.435   0.6642  
Age                 -0.011825   0.045472  -0.260   0.7952  
Gendermale           1.070173   0.863104   1.240   0.2168  
ORdate_epoch         0.001856   0.001096   1.693   0.0924 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.686 on 159 degrees of freedom
Multiple R-squared:  0.02936,   Adjusted R-squared:  0.004943 
F-statistic: 1.202 on 4 and 159 DF,  p-value: 0.3119

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: TNFA_rank 
Effect size...............: 0.161735 
Standard error............: 0.371858 
Odds ratio (effect size)..: 1.176 
Lower 95% CI..............: 0.567 
Upper 95% CI..............: 2.437 
T-value...................: 0.434937 
P-value...................: 0.664198 
R^2.......................: 0.029362 
Adjusted r^2..............: 0.004943 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing MIF_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.519110      0.001321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4140 -1.9811 -1.2029 -0.0171 30.6314 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.359e+01  1.091e+01  -1.246    0.214
currentDF[, TRAIT] -3.284e-03  3.251e-01  -0.010    0.992
Age                -3.167e-02  3.551e-02  -0.892    0.373
Gendermale          7.277e-01  6.703e-01   1.085    0.279
ORdate_epoch        1.375e-03  8.416e-04   1.633    0.104

Residual standard error: 4.381 on 225 degrees of freedom
Multiple R-squared:  0.02219,   Adjusted R-squared:  0.004808 
F-statistic: 1.277 on 4 and 225 DF,  p-value: 0.28

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MIF_rank 
Effect size...............: -0.003284 
Standard error............: 0.325134 
Odds ratio (effect size)..: 0.997 
Lower 95% CI..............: 0.527 
Upper 95% CI..............: 1.885 
T-value...................: -0.0101 
P-value...................: 0.9919505 
R^2.......................: 0.022191 
Adjusted r^2..............: 0.004808 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MCP1_rank
filter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.202277      0.001297  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3841 -2.0118 -1.2259 -0.0199 30.6167 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.316e+01  1.002e+01  -1.313   0.1905  
currentDF[, TRAIT] -4.605e-02  3.000e-01  -0.153   0.8781  
Age                -3.057e-02  3.606e-02  -0.848   0.3974  
Gendermale          7.207e-01  6.791e-01   1.061   0.2897  
ORdate_epoch        1.336e-03  7.702e-04   1.735   0.0842 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.399 on 223 degrees of freedom
Multiple R-squared:  0.02118,   Adjusted R-squared:  0.003627 
F-statistic: 1.207 on 4 and 223 DF,  p-value: 0.3089

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MCP1_rank 
Effect size...............: -0.046054 
Standard error............: 0.300037 
Odds ratio (effect size)..: 0.955 
Lower 95% CI..............: 0.53 
Upper 95% CI..............: 1.719 
T-value...................: -0.153494 
P-value...................: 0.8781474 
R^2.......................: 0.021184 
Adjusted r^2..............: 0.003627 
Sample size of AE DB......: 622 
Sample size of model......: 228 
Missing data %............: 63.34405 

- processing MIP1a_rank
filter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -13.44928       0.00124  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6098 -2.0117 -1.2591 -0.0956 30.4804 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.255e+01  1.017e+01  -1.235    0.218  
currentDF[, TRAIT]  1.249e-01  3.105e-01   0.402    0.688  
Age                -3.174e-02  3.777e-02  -0.840    0.402  
Gendermale          7.489e-01  7.227e-01   1.036    0.301  
ORdate_epoch        1.294e-03  7.811e-04   1.657    0.099 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.516 on 209 degrees of freedom
Multiple R-squared:  0.02134,   Adjusted R-squared:  0.002605 
F-statistic: 1.139 on 4 and 209 DF,  p-value: 0.3391

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 0.124914 
Standard error............: 0.310533 
Odds ratio (effect size)..: 1.133 
Lower 95% CI..............: 0.616 
Upper 95% CI..............: 2.082 
T-value...................: 0.402255 
P-value...................: 0.6879072 
R^2.......................: 0.021335 
Adjusted r^2..............: 0.002605 
Sample size of AE DB......: 622 
Sample size of model......: 214 
Missing data %............: 65.59485 

- processing RANTES_rank
filter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.167115      0.001295  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5560 -2.0074 -1.2026  0.0459 30.6277 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.700e+01  1.095e+01  -1.552    0.122  
currentDF[, TRAIT]  2.475e-01  3.290e-01   0.752    0.453  
Age                -2.622e-02  3.651e-02  -0.718    0.473  
Gendermale          7.406e-01  6.851e-01   1.081    0.281  
ORdate_epoch        1.617e-03  8.314e-04   1.946    0.053 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.409 on 221 degrees of freedom
Multiple R-squared:  0.02426,   Adjusted R-squared:  0.006601 
F-statistic: 1.374 on 4 and 221 DF,  p-value: 0.2439

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: RANTES_rank 
Effect size...............: 0.247523 
Standard error............: 0.328956 
Odds ratio (effect size)..: 1.281 
Lower 95% CI..............: 0.672 
Upper 95% CI..............: 2.441 
T-value...................: 0.752451 
P-value...................: 0.4525806 
R^2.......................: 0.024262 
Adjusted r^2..............: 0.006601 
Sample size of AE DB......: 622 
Sample size of model......: 226 
Missing data %............: 63.6656 

- processing MIG_rank
filter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.542930      0.001323  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8434 -2.1050 -1.2442  0.0958 30.2816 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.109e+01  1.013e+01  -1.094    0.275
currentDF[, TRAIT]  3.188e-01  3.109e-01   1.025    0.306
Age                -2.538e-02  3.611e-02  -0.703    0.483
Gendermale          6.862e-01  6.806e-01   1.008    0.314
ORdate_epoch        1.144e-03  7.904e-04   1.448    0.149

Residual standard error: 4.396 on 222 degrees of freedom
Multiple R-squared:  0.02698,   Adjusted R-squared:  0.009448 
F-statistic: 1.539 on 4 and 222 DF,  p-value: 0.1919

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MIG_rank 
Effect size...............: 0.318818 
Standard error............: 0.310908 
Odds ratio (effect size)..: 1.376 
Lower 95% CI..............: 0.748 
Upper 95% CI..............: 2.53 
T-value...................: 1.02544 
P-value...................: 0.3062716 
R^2.......................: 0.02698 
Adjusted r^2..............: 0.009448 
Sample size of AE DB......: 622 
Sample size of model......: 227 
Missing data %............: 63.50482 

- processing IP10_rank
filter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
         -14.74815             0.64339             0.00135  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6412 -2.1686 -1.2411  0.2919 30.1504 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.510e+01  1.042e+01  -1.449   0.1489  
currentDF[, TRAIT]  6.161e-01  3.209e-01   1.920   0.0563 .
Age                -1.884e-02  3.918e-02  -0.481   0.6311  
Gendermale          9.079e-01  7.154e-01   1.269   0.2059  
ORdate_epoch        1.426e-03  7.981e-04   1.787   0.0755 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.533 on 202 degrees of freedom
Multiple R-squared:  0.04241,   Adjusted R-squared:  0.02345 
F-statistic: 2.237 on 4 and 202 DF,  p-value: 0.06638

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IP10_rank 
Effect size...............: 0.616118 
Standard error............: 0.320928 
Odds ratio (effect size)..: 1.852 
Lower 95% CI..............: 0.987 
Upper 95% CI..............: 3.473 
T-value...................: 1.919802 
P-value...................: 0.05629171 
R^2.......................: 0.042411 
Adjusted r^2..............: 0.023449 
Sample size of AE DB......: 622 
Sample size of model......: 207 
Missing data %............: 66.72026 

- processing Eotaxin1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.519110      0.001321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4142 -1.9931 -1.2138 -0.0131 30.6133 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.354e+01  9.848e+00  -1.375    0.171  
currentDF[, TRAIT]  2.062e-02  2.949e-01   0.070    0.944  
Age                -3.150e-02  3.553e-02  -0.887    0.376  
Gendermale          7.234e-01  6.731e-01   1.075    0.284  
ORdate_epoch        1.370e-03  7.605e-04   1.802    0.073 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.381 on 225 degrees of freedom
Multiple R-squared:  0.02221,   Adjusted R-squared:  0.004829 
F-statistic: 1.278 on 4 and 225 DF,  p-value: 0.2795

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 0.020624 
Standard error............: 0.294942 
Odds ratio (effect size)..: 1.021 
Lower 95% CI..............: 0.573 
Upper 95% CI..............: 1.82 
T-value...................: 0.069927 
P-value...................: 0.9443139 
R^2.......................: 0.022212 
Adjusted r^2..............: 0.004829 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing TARC_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      2.256  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5768 -2.1300 -1.3717 -0.0275 30.4690 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.112e+01  1.315e+01  -0.846    0.399
currentDF[, TRAIT]  8.094e-02  3.536e-01   0.229    0.819
Age                -3.457e-02  4.055e-02  -0.852    0.395
Gendermale          8.457e-01  7.618e-01   1.110    0.268
ORdate_epoch        1.195e-03  9.933e-04   1.203    0.230

Residual standard error: 4.637 on 198 degrees of freedom
Multiple R-squared:  0.01726,   Adjusted R-squared:  -0.002597 
F-statistic: 0.8692 on 4 and 198 DF,  p-value: 0.4834

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: TARC_rank 
Effect size...............: 0.080943 
Standard error............: 0.353617 
Odds ratio (effect size)..: 1.084 
Lower 95% CI..............: 0.542 
Upper 95% CI..............: 2.168 
T-value...................: 0.2289 
P-value...................: 0.8191829 
R^2.......................: 0.017256 
Adjusted r^2..............: -0.002597 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing PARC_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -20.837956            0.578566            0.001825  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0767 -2.0612 -1.1306  0.0867 30.6517 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -2.059e+01  1.031e+01  -1.997   0.0470 *
currentDF[, TRAIT]  5.945e-01  3.067e-01   1.938   0.0539 .
Age                -2.711e-02  3.525e-02  -0.769   0.4428  
Gendermale          8.345e-01  6.670e-01   1.251   0.2122  
ORdate_epoch        1.901e-03  7.919e-04   2.400   0.0172 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.345 on 225 degrees of freedom
Multiple R-squared:  0.03824,   Adjusted R-squared:  0.02115 
F-statistic: 2.237 on 4 and 225 DF,  p-value: 0.06596

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: PARC_rank 
Effect size...............: 0.594455 
Standard error............: 0.306749 
Odds ratio (effect size)..: 1.812 
Lower 95% CI..............: 0.993 
Upper 95% CI..............: 3.306 
T-value...................: 1.937918 
P-value...................: 0.05388457 
R^2.......................: 0.038244 
Adjusted r^2..............: 0.021146 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MDC_rank
filter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -13.696173      0.001261  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-3.357 -2.085 -1.206 -0.141 30.611 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.167e+01  1.071e+01  -1.090    0.277
currentDF[, TRAIT] -1.240e-01  3.258e-01  -0.381    0.704
Age                -3.240e-02  3.785e-02  -0.856    0.393
Gendermale          7.928e-01  7.156e-01   1.108    0.269
ORdate_epoch        1.226e-03  8.204e-04   1.495    0.137

Residual standard error: 4.511 on 210 degrees of freedom
Multiple R-squared:  0.02196,   Adjusted R-squared:  0.003331 
F-statistic: 1.179 on 4 and 210 DF,  p-value: 0.3212

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MDC_rank 
Effect size...............: -0.124021 
Standard error............: 0.325763 
Odds ratio (effect size)..: 0.883 
Lower 95% CI..............: 0.466 
Upper 95% CI..............: 1.673 
T-value...................: -0.380708 
P-value...................: 0.7038051 
R^2.......................: 0.02196 
Adjusted r^2..............: 0.003331 
Sample size of AE DB......: 622 
Sample size of model......: 215 
Missing data %............: 65.43408 

- processing OPG_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.519110      0.001321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5504 -2.0188 -1.2085  0.0468 30.6624 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.361e+01  9.743e+00  -1.396   0.1640  
currentDF[, TRAIT]  1.050e-01  2.922e-01   0.359   0.7198  
Age                -3.013e-02  3.571e-02  -0.844   0.3998  
Gendermale          7.163e-01  6.708e-01   1.068   0.2868  
ORdate_epoch        1.368e-03  7.511e-04   1.822   0.0698 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.38 on 225 degrees of freedom
Multiple R-squared:  0.02275,   Adjusted R-squared:  0.005378 
F-statistic:  1.31 on 4 and 225 DF,  p-value: 0.2673

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: OPG_rank 
Effect size...............: 0.104952 
Standard error............: 0.292183 
Odds ratio (effect size)..: 1.111 
Lower 95% CI..............: 0.626 
Upper 95% CI..............: 1.969 
T-value...................: 0.359197 
P-value...................: 0.7197843 
R^2.......................: 0.022751 
Adjusted r^2..............: 0.005378 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing sICAM1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.519110      0.001321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4487 -1.9827 -1.1965 -0.0319 30.6501 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.231e+01  1.005e+01  -1.225   0.2219  
currentDF[, TRAIT] -1.599e-01  2.999e-01  -0.533   0.5945  
Age                -3.441e-02  3.582e-02  -0.961   0.3378  
Gendermale          7.117e-01  6.705e-01   1.061   0.2896  
ORdate_epoch        1.289e-03  7.689e-04   1.676   0.0951 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.378 on 225 degrees of freedom
Multiple R-squared:  0.02342,   Adjusted R-squared:  0.006063 
F-statistic: 1.349 on 4 and 225 DF,  p-value: 0.2526

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -0.159873 
Standard error............: 0.299935 
Odds ratio (effect size)..: 0.852 
Lower 95% CI..............: 0.473 
Upper 95% CI..............: 1.534 
T-value...................: -0.533025 
P-value...................: 0.5945424 
R^2.......................: 0.023424 
Adjusted r^2..............: 0.006063 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing VEGFA_rank
filter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -13.17855       0.00122  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5095 -2.0618 -1.2743 -0.0773 30.6924 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1.569e+01  1.211e+01  -1.296    0.196
currentDF[, TRAIT] -1.890e-01  3.583e-01  -0.527    0.598
Age                -2.861e-02  3.971e-02  -0.721    0.472
Gendermale          5.617e-01  7.408e-01   0.758    0.449
ORdate_epoch        1.541e-03  9.516e-04   1.620    0.107

Residual standard error: 4.549 on 196 degrees of freedom
Multiple R-squared:  0.01685,   Adjusted R-squared:  -0.003216 
F-statistic: 0.8397 on 4 and 196 DF,  p-value: 0.5015

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -0.188959 
Standard error............: 0.358267 
Odds ratio (effect size)..: 0.828 
Lower 95% CI..............: 0.41 
Upper 95% CI..............: 1.671 
T-value...................: -0.527426 
P-value...................: 0.5984946 
R^2.......................: 0.016848 
Adjusted r^2..............: -0.003216 
Sample size of AE DB......: 622 
Sample size of model......: 201 
Missing data %............: 67.68489 

- processing TGFB_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -15.40295       0.00139  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3750 -1.9905 -1.2464  0.0107 30.6552 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.431e+01  9.749e+00  -1.468   0.1435  
currentDF[, TRAIT] -1.262e-01  2.926e-01  -0.431   0.6666  
Age                -2.329e-02  3.527e-02  -0.660   0.5097  
Gendermale          5.968e-01  6.569e-01   0.908   0.3646  
ORdate_epoch        1.394e-03  7.529e-04   1.851   0.0654 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.345 on 226 degrees of freedom
Multiple R-squared:  0.02174,   Adjusted R-squared:  0.004424 
F-statistic: 1.256 on 4 and 226 DF,  p-value: 0.2884

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: TGFB_rank 
Effect size...............: -0.126227 
Standard error............: 0.292592 
Odds ratio (effect size)..: 0.881 
Lower 95% CI..............: 0.497 
Upper 95% CI..............: 1.564 
T-value...................: -0.431409 
P-value...................: 0.6665824 
R^2.......................: 0.021739 
Adjusted r^2..............: 0.004424 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP2_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -16.834255      0.001508  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4292 -1.9395 -1.1981  0.0116 30.3463 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.405e+01  9.874e+00  -1.423   0.1562  
currentDF[, TRAIT] -2.517e-01  2.937e-01  -0.857   0.3923  
Age                -3.660e-02  3.498e-02  -1.046   0.2965  
Gendermale          5.305e-01  6.612e-01   0.802   0.4232  
ORdate_epoch        1.452e-03  7.688e-04   1.889   0.0602 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.327 on 226 degrees of freedom
Multiple R-squared:  0.02826,   Adjusted R-squared:  0.01106 
F-statistic: 1.643 on 4 and 226 DF,  p-value: 0.1643

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MMP2_rank 
Effect size...............: -0.251714 
Standard error............: 0.29366 
Odds ratio (effect size)..: 0.777 
Lower 95% CI..............: 0.437 
Upper 95% CI..............: 1.382 
T-value...................: -0.857161 
P-value...................: 0.3922639 
R^2.......................: 0.028263 
Adjusted r^2..............: 0.011064 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP8_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -16.834255      0.001508  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5421 -1.9580 -1.1950  0.1059 30.1261 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.538e+01  9.715e+00  -1.583   0.1148  
currentDF[, TRAIT]  2.445e-01  2.875e-01   0.850   0.3960  
Age                -3.338e-02  3.485e-02  -0.958   0.3391  
Gendermale          5.559e-01  6.573e-01   0.846   0.3986  
ORdate_epoch        1.539e-03  7.597e-04   2.026   0.0439 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.328 on 226 degrees of freedom
Multiple R-squared:  0.02821,   Adjusted R-squared:  0.01101 
F-statistic:  1.64 on 4 and 226 DF,  p-value: 0.165

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MMP8_rank 
Effect size...............: 0.244472 
Standard error............: 0.287467 
Odds ratio (effect size)..: 1.277 
Lower 95% CI..............: 0.727 
Upper 95% CI..............: 2.243 
T-value...................: 0.850438 
P-value...................: 0.3959819 
R^2.......................: 0.028214 
Adjusted r^2..............: 0.011014 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP9_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -18.416484            0.435535            0.001634  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8374 -2.0169 -1.1709  0.1997 29.7487 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.720e+01  9.738e+00  -1.766   0.0787 .
currentDF[, TRAIT]  4.332e-01  2.863e-01   1.513   0.1316  
Age                -3.317e-02  3.472e-02  -0.955   0.3405  
Gendermale          6.247e-01  6.509e-01   0.960   0.3382  
ORdate_epoch        1.679e-03  7.614e-04   2.206   0.0284 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.313 on 226 degrees of freedom
Multiple R-squared:  0.03488,   Adjusted R-squared:  0.0178 
F-statistic: 2.042 on 4 and 226 DF,  p-value: 0.08943

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MMP9_rank 
Effect size...............: 0.433206 
Standard error............: 0.286292 
Odds ratio (effect size)..: 1.542 
Lower 95% CI..............: 0.88 
Upper 95% CI..............: 2.703 
T-value...................: 1.513162 
P-value...................: 0.1316357 
R^2.......................: 0.034882 
Adjusted r^2..............: 0.0178 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

Analysis of PCSK9.

- processing IL2_rank
filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   9.1541829    -0.0006487  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7997 -1.1141 -0.6010  0.3752 11.7523 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        10.6867612  5.2073117   2.052   0.0416 *
currentDF[, TRAIT] -0.1399590  0.1445148  -0.968   0.3341  
Age                -0.0130821  0.0174453  -0.750   0.4543  
Gendermale          0.1986290  0.3384235   0.587   0.5580  
ORdate_epoch       -0.0007128  0.0004119  -1.730   0.0853 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.91 on 177 degrees of freedom
Multiple R-squared:  0.02459,   Adjusted R-squared:  0.002545 
F-statistic: 1.115 on 4 and 177 DF,  p-value: 0.3508

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL2_rank 
Effect size...............: -0.139959 
Standard error............: 0.144515 
Odds ratio (effect size)..: 0.869 
Lower 95% CI..............: 0.655 
Upper 95% CI..............: 1.154 
T-value...................: -0.968475 
P-value...................: 0.3341286 
R^2.......................: 0.024588 
Adjusted r^2..............: 0.002545 
Sample size of AE DB......: 622 
Sample size of model......: 182 
Missing data %............: 70.73955 

- processing IL4_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   9.6404719    -0.0006855  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7020 -1.1374 -0.5469  0.3174 11.5282 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        11.3407209  5.9349710   1.911   0.0578 .
currentDF[, TRAIT] -0.0817999  0.1606905  -0.509   0.6114  
Age                -0.0182960  0.0192327  -0.951   0.3429  
Gendermale          0.2853139  0.3772642   0.756   0.4506  
ORdate_epoch       -0.0007401  0.0004710  -1.571   0.1181  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.991 on 159 degrees of freedom
Multiple R-squared:  0.02357,   Adjusted R-squared:  -0.0009991 
F-statistic: 0.9593 on 4 and 159 DF,  p-value: 0.4316

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL4_rank 
Effect size...............: -0.0818 
Standard error............: 0.160691 
Odds ratio (effect size)..: 0.921 
Lower 95% CI..............: 0.672 
Upper 95% CI..............: 1.263 
T-value...................: -0.509052 
P-value...................: 0.6114211 
R^2.......................: 0.023565 
Adjusted r^2..............: -0.000999 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL5_rank
filter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  11.6334487    -0.0008481  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8517 -1.0651 -0.5250  0.3153 11.5924 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        13.4143680  5.4162244   2.477   0.0142 *
currentDF[, TRAIT] -0.1381720  0.1477893  -0.935   0.3511  
Age                -0.0208367  0.0178587  -1.167   0.2449  
Gendermale          0.3078135  0.3470472   0.887   0.3763  
ORdate_epoch       -0.0008968  0.0004341  -2.066   0.0403 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.928 on 174 degrees of freedom
Multiple R-squared:  0.03763,   Adjusted R-squared:  0.01551 
F-statistic: 1.701 on 4 and 174 DF,  p-value: 0.1519

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL5_rank 
Effect size...............: -0.138172 
Standard error............: 0.147789 
Odds ratio (effect size)..: 0.871 
Lower 95% CI..............: 0.652 
Upper 95% CI..............: 1.164 
T-value...................: -0.934926 
P-value...................: 0.3511223 
R^2.......................: 0.03763 
Adjusted r^2..............: 0.015506 
Sample size of AE DB......: 622 
Sample size of model......: 179 
Missing data %............: 71.22186 

- processing IL6_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            1.0899             -0.2773  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7903 -1.0887 -0.4857  0.3843 11.8563 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.2459711  4.6722596   1.765   0.0792 .
currentDF[, TRAIT] -0.2588347  0.1393575  -1.857   0.0649 .
Age                -0.0198254  0.0168730  -1.175   0.2415  
Gendermale          0.1425333  0.3144809   0.453   0.6509  
ORdate_epoch       -0.0004749  0.0003729  -1.273   0.2045  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.864 on 184 degrees of freedom
Multiple R-squared:  0.03954,   Adjusted R-squared:  0.01866 
F-statistic: 1.894 on 4 and 184 DF,  p-value: 0.1134

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL6_rank 
Effect size...............: -0.258835 
Standard error............: 0.139358 
Odds ratio (effect size)..: 0.772 
Lower 95% CI..............: 0.587 
Upper 95% CI..............: 1.014 
T-value...................: -1.857343 
P-value...................: 0.06486054 
R^2.......................: 0.039537 
Adjusted r^2..............: 0.018657 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing IL8_rank
filter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   9.6599386    -0.0006799  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9041 -1.1647 -0.4570  0.3999 11.2083 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)        11.356649   5.031639   2.257   0.0252 *
currentDF[, TRAIT]  0.117346   0.150181   0.781   0.4356  
Age                -0.014951   0.017185  -0.870   0.3855  
Gendermale          0.117391   0.341964   0.343   0.7318  
ORdate_epoch       -0.000742   0.000400  -1.855   0.0652 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.925 on 175 degrees of freedom
Multiple R-squared:  0.02632,   Adjusted R-squared:  0.004061 
F-statistic: 1.182 on 4 and 175 DF,  p-value: 0.3202

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL8_rank 
Effect size...............: 0.117346 
Standard error............: 0.150181 
Odds ratio (effect size)..: 1.125 
Lower 95% CI..............: 0.838 
Upper 95% CI..............: 1.509 
T-value...................: 0.781363 
P-value...................: 0.4356446 
R^2.......................: 0.026317 
Adjusted r^2..............: 0.004061 
Sample size of AE DB......: 622 
Sample size of model......: 180 
Missing data %............: 71.06109 

- processing IL9_rank
filter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)

Coefficients:
(Intercept)          Age  
    2.79897     -0.02431  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8385 -1.1789 -0.4332  0.5509 11.2748 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         7.9306212  4.1754537   1.899   0.0589 .
currentDF[, TRAIT]  0.1194181  0.1284175   0.930   0.3535  
Age                -0.0226491  0.0158432  -1.430   0.1544  
Gendermale          0.0753740  0.2952192   0.255   0.7987  
ORdate_epoch       -0.0004216  0.0003197  -1.319   0.1887  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.843 on 205 degrees of freedom
Multiple R-squared:  0.02499,   Adjusted R-squared:  0.005965 
F-statistic: 1.314 on 4 and 205 DF,  p-value: 0.2661

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL9_rank 
Effect size...............: 0.119418 
Standard error............: 0.128418 
Odds ratio (effect size)..: 1.127 
Lower 95% CI..............: 0.876 
Upper 95% CI..............: 1.449 
T-value...................: 0.92992 
P-value...................: 0.3535061 
R^2.......................: 0.024989 
Adjusted r^2..............: 0.005965 
Sample size of AE DB......: 622 
Sample size of model......: 210 
Missing data %............: 66.23794 

- processing IL10_rank
filter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  10.8438119    -0.0007837  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7342 -1.1622 -0.5555  0.2723 11.5111 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        13.1676316  6.4097410   2.054   0.0417 *
currentDF[, TRAIT] -0.1138161  0.1687950  -0.674   0.5012  
Age                -0.0188995  0.0200846  -0.941   0.3482  
Gendermale          0.2384553  0.3891907   0.613   0.5410  
ORdate_epoch       -0.0008828  0.0005031  -1.755   0.0813 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.028 on 152 degrees of freedom
Multiple R-squared:  0.02643,   Adjusted R-squared:  0.0008145 
F-statistic: 1.032 on 4 and 152 DF,  p-value: 0.3928

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL10_rank 
Effect size...............: -0.113816 
Standard error............: 0.168795 
Odds ratio (effect size)..: 0.892 
Lower 95% CI..............: 0.641 
Upper 95% CI..............: 1.242 
T-value...................: -0.674286 
P-value...................: 0.5011535 
R^2.......................: 0.026435 
Adjusted r^2..............: 0.000814 
Sample size of AE DB......: 622 
Sample size of model......: 157 
Missing data %............: 74.75884 

- processing IL12_rank
filter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  11.3951334    -0.0008278  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8102 -1.1174 -0.5299  0.3320 11.4146 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        13.4623892  5.9089279   2.278   0.0240 *
currentDF[, TRAIT] -0.0749530  0.1578944  -0.475   0.6356  
Age                -0.0203907  0.0191307  -1.066   0.2881  
Gendermale          0.3143598  0.3685102   0.853   0.3949  
ORdate_epoch       -0.0009019  0.0004678  -1.928   0.0556 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.976 on 161 degrees of freedom
Multiple R-squared:  0.03099,   Adjusted R-squared:  0.006913 
F-statistic: 1.287 on 4 and 161 DF,  p-value: 0.2773

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL12_rank 
Effect size...............: -0.074953 
Standard error............: 0.157894 
Odds ratio (effect size)..: 0.928 
Lower 95% CI..............: 0.681 
Upper 95% CI..............: 1.264 
T-value...................: -0.474703 
P-value...................: 0.6356417 
R^2.......................: 0.030988 
Adjusted r^2..............: 0.006913 
Sample size of AE DB......: 622 
Sample size of model......: 166 
Missing data %............: 73.3119 

- processing IL13_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2356131    -0.0214781    -0.0004477  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6806 -1.1517 -0.4245  0.4343 11.3076 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.2746374  4.0421179   2.047   0.0418 *
currentDF[, TRAIT]  0.0772504  0.1214434   0.636   0.5254  
Age                -0.0203337  0.0147919  -1.375   0.1706  
Gendermale          0.0522362  0.2781823   0.188   0.8512  
ORdate_epoch       -0.0004601  0.0003120  -1.475   0.1417  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared:  0.02063,   Adjusted R-squared:  0.003218 
F-statistic: 1.185 on 4 and 225 DF,  p-value: 0.3183

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL13_rank 
Effect size...............: 0.07725 
Standard error............: 0.121443 
Odds ratio (effect size)..: 1.08 
Lower 95% CI..............: 0.851 
Upper 95% CI..............: 1.371 
T-value...................: 0.636102 
P-value...................: 0.5253564 
R^2.......................: 0.020629 
Adjusted r^2..............: 0.003218 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing IL21_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2356131    -0.0214781    -0.0004477  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7033 -1.1692 -0.4272  0.4686 11.2812 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.2404724  4.0383103   2.041   0.0425 *
currentDF[, TRAIT]  0.0837819  0.1210152   0.692   0.4894  
Age                -0.0205102  0.0147481  -1.391   0.1657  
Gendermale          0.0484180  0.2784293   0.174   0.8621  
ORdate_epoch       -0.0004562  0.0003114  -1.465   0.1443  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared:  0.02095,   Adjusted R-squared:  0.003548 
F-statistic: 1.204 on 4 and 225 DF,  p-value: 0.31

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL21_rank 
Effect size...............: 0.083782 
Standard error............: 0.121015 
Odds ratio (effect size)..: 1.087 
Lower 95% CI..............: 0.858 
Upper 95% CI..............: 1.378 
T-value...................: 0.692325 
P-value...................: 0.4894468 
R^2.......................: 0.020953 
Adjusted r^2..............: 0.003548 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing INFG_rank
filter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  10.3147192    -0.0007374  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8319 -1.1394 -0.5288  0.3587 11.3976 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        12.3218069  5.6080023   2.197   0.0294 *
currentDF[, TRAIT] -0.0527830  0.1617470  -0.326   0.7446  
Age                -0.0196304  0.0185450  -1.059   0.2913  
Gendermale          0.2705697  0.3694210   0.732   0.4649  
ORdate_epoch       -0.0008088  0.0004412  -1.833   0.0685 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.962 on 168 degrees of freedom
Multiple R-squared:  0.02829,   Adjusted R-squared:  0.005151 
F-statistic: 1.223 on 4 and 168 DF,  p-value: 0.3031

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: INFG_rank 
Effect size...............: -0.052783 
Standard error............: 0.161747 
Odds ratio (effect size)..: 0.949 
Lower 95% CI..............: 0.691 
Upper 95% CI..............: 1.302 
T-value...................: -0.32633 
P-value...................: 0.7445806 
R^2.......................: 0.028287 
Adjusted r^2..............: 0.005151 
Sample size of AE DB......: 622 
Sample size of model......: 173 
Missing data %............: 72.18649 

- processing TNFA_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  11.3929054    -0.0008265  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7874 -1.1013 -0.5387  0.3471 11.4889 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        12.9241512  5.8974847   2.191   0.0299 *
currentDF[, TRAIT] -0.0745333  0.1577660  -0.472   0.6373  
Age                -0.0179412  0.0192921  -0.930   0.3538  
Gendermale          0.3363122  0.3661845   0.918   0.3598  
ORdate_epoch       -0.0008722  0.0004650  -1.875   0.0626 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.988 on 159 degrees of freedom
Multiple R-squared:  0.03068,   Adjusted R-squared:  0.00629 
F-statistic: 1.258 on 4 and 159 DF,  p-value: 0.2889

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: TNFA_rank 
Effect size...............: -0.074533 
Standard error............: 0.157766 
Odds ratio (effect size)..: 0.928 
Lower 95% CI..............: 0.681 
Upper 95% CI..............: 1.265 
T-value...................: -0.472429 
P-value...................: 0.6372681 
R^2.......................: 0.030675 
Adjusted r^2..............: 0.00629 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing MIF_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            1.1565              0.2603  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8792 -1.1237 -0.4763  0.4106 11.1418 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)         4.8297210  4.4943802   1.075    0.284
currentDF[, TRAIT]  0.2193344  0.1339830   1.637    0.103
Age                -0.0203267  0.0146314  -1.389    0.166
Gendermale          0.0713649  0.2762382   0.258    0.796
ORdate_epoch       -0.0001870  0.0003468  -0.539    0.590

Residual standard error: 1.805 on 225 degrees of freedom
Multiple R-squared:  0.03042,   Adjusted R-squared:  0.01318 
F-statistic: 1.765 on 4 and 225 DF,  p-value: 0.1369

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MIF_rank 
Effect size...............: 0.219334 
Standard error............: 0.133983 
Odds ratio (effect size)..: 1.245 
Lower 95% CI..............: 0.958 
Upper 95% CI..............: 1.619 
T-value...................: 1.637031 
P-value...................: 0.1030218 
R^2.......................: 0.030416 
Adjusted r^2..............: 0.013179 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MCP1_rank
filter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   7.0892859    -0.0004715  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-1.862 -1.120 -0.423  0.404 11.369 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         7.5157309  4.1426209   1.814    0.071 .
currentDF[, TRAIT]  0.1095011  0.1240198   0.883    0.378  
Age                -0.0191122  0.0149044  -1.282    0.201  
Gendermale          0.0340664  0.2806999   0.121    0.904  
ORdate_epoch       -0.0004044  0.0003183  -1.270    0.205  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.818 on 223 degrees of freedom
Multiple R-squared:  0.0219,    Adjusted R-squared:  0.004358 
F-statistic: 1.248 on 4 and 223 DF,  p-value: 0.2914

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MCP1_rank 
Effect size...............: 0.109501 
Standard error............: 0.12402 
Odds ratio (effect size)..: 1.116 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 1.423 
T-value...................: 0.882932 
P-value...................: 0.3782236 
R^2.......................: 0.021903 
Adjusted r^2..............: 0.004358 
Sample size of AE DB......: 622 
Sample size of model......: 228 
Missing data %............: 63.34405 

- processing MIP1a_rank
filter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)

Coefficients:
(Intercept)          Age  
    2.64159     -0.02216  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7388 -1.1640 -0.4069  0.4387 11.2726 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         7.9169097  4.1229287   1.920   0.0562 .
currentDF[, TRAIT]  0.1023908  0.1259402   0.813   0.4171  
Age                -0.0214162  0.0153191  -1.398   0.1636  
Gendermale          0.0501928  0.2930888   0.171   0.8642  
ORdate_epoch       -0.0004268  0.0003168  -1.347   0.1794  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.831 on 209 degrees of freedom
Multiple R-squared:  0.02159,   Adjusted R-squared:  0.002867 
F-statistic: 1.153 on 4 and 209 DF,  p-value: 0.3327

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 0.102391 
Standard error............: 0.12594 
Odds ratio (effect size)..: 1.108 
Lower 95% CI..............: 0.865 
Upper 95% CI..............: 1.418 
T-value...................: 0.813011 
P-value...................: 0.4171367 
R^2.......................: 0.021593 
Adjusted r^2..............: 0.002867 
Sample size of AE DB......: 622 
Sample size of model......: 214 
Missing data %............: 65.59485 

- processing RANTES_rank
filter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2010798    -0.0208171    -0.0004485  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7193 -1.1356 -0.4676  0.4209 11.1652 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.5588814  4.5372531   1.886   0.0606 .
currentDF[, TRAIT] -0.0280877  0.1363121  -0.206   0.8369  
Age                -0.0214415  0.0151282  -1.417   0.1578  
Gendermale          0.0302855  0.2838968   0.107   0.9151  
ORdate_epoch       -0.0004755  0.0003445  -1.380   0.1689  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.827 on 221 degrees of freedom
Multiple R-squared:  0.01838,   Adjusted R-squared:  0.00061 
F-statistic: 1.034 on 4 and 221 DF,  p-value: 0.3903

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: RANTES_rank 
Effect size...............: -0.028088 
Standard error............: 0.136312 
Odds ratio (effect size)..: 0.972 
Lower 95% CI..............: 0.744 
Upper 95% CI..............: 1.27 
T-value...................: -0.206055 
P-value...................: 0.836938 
R^2.......................: 0.018377 
Adjusted r^2..............: 0.00061 
Sample size of AE DB......: 622 
Sample size of model......: 226 
Missing data %............: 63.6656 

- processing MIG_rank
filter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.1935014    -0.0220026    -0.0004427  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7752 -1.1603 -0.4243  0.4936 11.2843 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         9.2902521  4.1605284   2.233   0.0266 *
currentDF[, TRAIT]  0.1390263  0.1276657   1.089   0.2773  
Age                -0.0192866  0.0148284  -1.301   0.1947  
Gendermale          0.0129311  0.2794631   0.046   0.9631  
ORdate_epoch       -0.0005455  0.0003246  -1.681   0.0942 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.805 on 222 degrees of freedom
Multiple R-squared:  0.02456,   Adjusted R-squared:  0.006987 
F-statistic: 1.398 on 4 and 222 DF,  p-value: 0.2357

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MIG_rank 
Effect size...............: 0.139026 
Standard error............: 0.127666 
Odds ratio (effect size)..: 1.149 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.476 
T-value...................: 1.088987 
P-value...................: 0.2773405 
R^2.......................: 0.024562 
Adjusted r^2..............: 0.006987 
Sample size of AE DB......: 622 
Sample size of model......: 227 
Missing data %............: 63.50482 

- processing IP10_rank
filter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)

Coefficients:
(Intercept)          Age  
    2.94988     -0.02668  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8386 -1.1099 -0.3646  0.3677 11.2761 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         7.130193   4.191749   1.701   0.0905 .
currentDF[, TRAIT]  0.020848   0.129070   0.162   0.8718  
Age                -0.026232   0.015757  -1.665   0.0975 .
Gendermale          0.097008   0.287705   0.337   0.7363  
ORdate_epoch       -0.000341   0.000321  -1.062   0.2893  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.823 on 202 degrees of freedom
Multiple R-squared:  0.02076,   Adjusted R-squared:  0.001371 
F-statistic: 1.071 on 4 and 202 DF,  p-value: 0.3721

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IP10_rank 
Effect size...............: 0.020848 
Standard error............: 0.12907 
Odds ratio (effect size)..: 1.021 
Lower 95% CI..............: 0.793 
Upper 95% CI..............: 1.315 
T-value...................: 0.161526 
P-value...................: 0.8718405 
R^2.......................: 0.020761 
Adjusted r^2..............: 0.001371 
Sample size of AE DB......: 622 
Sample size of model......: 207 
Missing data %............: 66.72026 

- processing Eotaxin1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2356131    -0.0214781    -0.0004477  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7632 -1.1644 -0.4426  0.4722 11.2333 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.5696251  4.0773889   2.102   0.0367 *
currentDF[, TRAIT]  0.0905360  0.1221154   0.741   0.4592  
Age                -0.0207770  0.0147117  -1.412   0.1592  
Gendermale          0.0446076  0.2787027   0.160   0.8730  
ORdate_epoch       -0.0004807  0.0003149  -1.527   0.1282  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared:  0.02126,   Adjusted R-squared:  0.003859 
F-statistic: 1.222 on 4 and 225 DF,  p-value: 0.3024

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 0.090536 
Standard error............: 0.122115 
Odds ratio (effect size)..: 1.095 
Lower 95% CI..............: 0.862 
Upper 95% CI..............: 1.391 
T-value...................: 0.741397 
P-value...................: 0.4592259 
R^2.......................: 0.021259 
Adjusted r^2..............: 0.003859 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing TARC_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
            0.8051              0.1672              0.3894  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6552 -1.0730 -0.4158  0.5553  8.7697 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)         1.670e+00  4.748e+00   0.352    0.725
currentDF[, TRAIT]  1.596e-01  1.277e-01   1.250    0.213
Age                 1.933e-03  1.464e-02   0.132    0.895
Gendermale          3.852e-01  2.750e-01   1.400    0.163
ORdate_epoch       -7.866e-05  3.586e-04  -0.219    0.827

Residual standard error: 1.674 on 198 degrees of freedom
Multiple R-squared:  0.01939,   Adjusted R-squared:  -0.0004253 
F-statistic: 0.9785 on 4 and 198 DF,  p-value: 0.4203

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: TARC_rank 
Effect size...............: 0.159618 
Standard error............: 0.127674 
Odds ratio (effect size)..: 1.173 
Lower 95% CI..............: 0.913 
Upper 95% CI..............: 1.507 
T-value...................: 1.250192 
P-value...................: 0.2127049 
R^2.......................: 0.019385 
Adjusted r^2..............: -0.000425 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing PARC_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2356131    -0.0214781    -0.0004477  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7946 -1.1260 -0.4183  0.4067 11.3391 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         7.3076890  4.3058617   1.697   0.0911 .
currentDF[, TRAIT]  0.0706332  0.1281280   0.551   0.5820  
Age                -0.0209004  0.0147251  -1.419   0.1572  
Gendermale          0.0764582  0.2786028   0.274   0.7840  
ORdate_epoch       -0.0003815  0.0003308  -1.153   0.2500  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.815 on 225 degrees of freedom
Multiple R-squared:  0.02019,   Adjusted R-squared:  0.002772 
F-statistic: 1.159 on 4 and 225 DF,  p-value: 0.3297

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: PARC_rank 
Effect size...............: 0.070633 
Standard error............: 0.128128 
Odds ratio (effect size)..: 1.073 
Lower 95% CI..............: 0.835 
Upper 95% CI..............: 1.38 
T-value...................: 0.551271 
P-value...................: 0.5819951 
R^2.......................: 0.020191 
Adjusted r^2..............: 0.002772 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MDC_rank
filter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            1.1349              0.2423  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9500 -1.1275 -0.4380  0.5649 11.2986 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)         5.8576725  4.3265126   1.354    0.177
currentDF[, TRAIT]  0.1957865  0.1316427   1.487    0.138
Age                -0.0205178  0.0152965  -1.341    0.181
Gendermale          0.0923191  0.2891587   0.319    0.750
ORdate_epoch       -0.0002710  0.0003315  -0.817    0.415

Residual standard error: 1.823 on 210 degrees of freedom
Multiple R-squared:  0.0294,    Adjusted R-squared:  0.01092 
F-statistic:  1.59 on 4 and 210 DF,  p-value: 0.178

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MDC_rank 
Effect size...............: 0.195787 
Standard error............: 0.131643 
Odds ratio (effect size)..: 1.216 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.574 
T-value...................: 1.487257 
P-value...................: 0.138447 
R^2.......................: 0.029403 
Adjusted r^2..............: 0.010916 
Sample size of AE DB......: 622 
Sample size of model......: 215 
Missing data %............: 65.43408 

- processing OPG_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2356131    -0.0214781    -0.0004477  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7537 -1.1427 -0.3975  0.3883 11.1499 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.1229745  4.0390876   2.011   0.0455 *
currentDF[, TRAIT] -0.0344514  0.1211326  -0.284   0.7764  
Age                -0.0219421  0.0148051  -1.482   0.1397  
Gendermale          0.0675602  0.2781073   0.243   0.8083  
ORdate_epoch       -0.0004403  0.0003114  -1.414   0.1588  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.816 on 225 degrees of freedom
Multiple R-squared:  0.01922,   Adjusted R-squared:  0.001784 
F-statistic: 1.102 on 4 and 225 DF,  p-value: 0.3563

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: OPG_rank 
Effect size...............: -0.034451 
Standard error............: 0.121133 
Odds ratio (effect size)..: 0.966 
Lower 95% CI..............: 0.762 
Upper 95% CI..............: 1.225 
T-value...................: -0.284411 
P-value...................: 0.776357 
R^2.......................: 0.01922 
Adjusted r^2..............: 0.001784 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing sICAM1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)           Age  ORdate_epoch  
   8.2356131    -0.0214781    -0.0004477  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8209 -1.1109 -0.4214  0.3886 11.2455 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         7.4829984  4.1653632   1.796   0.0738 .
currentDF[, TRAIT]  0.0785145  0.1243014   0.632   0.5283  
Age                -0.0200872  0.0148448  -1.353   0.1774  
Gendermale          0.0716794  0.2778749   0.258   0.7967  
ORdate_epoch       -0.0003996  0.0003187  -1.254   0.2112  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.814 on 225 degrees of freedom
Multiple R-squared:  0.0206,    Adjusted R-squared:  0.003193 
F-statistic: 1.183 on 4 and 225 DF,  p-value: 0.3189

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 0.078514 
Standard error............: 0.124301 
Odds ratio (effect size)..: 1.082 
Lower 95% CI..............: 0.848 
Upper 95% CI..............: 1.38 
T-value...................: 0.631646 
P-value...................: 0.5282593 
R^2.......................: 0.020605 
Adjusted r^2..............: 0.003193 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing VEGFA_rank
filter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      1.114  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.4236 -1.1022 -0.2840  0.1626  8.8644 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)         5.1921938  4.5135792   1.150    0.251
currentDF[, TRAIT] -0.0208112  0.1335547  -0.156    0.876
Age                -0.0067017  0.0148019  -0.453    0.651
Gendermale          0.1381205  0.2761542   0.500    0.618
ORdate_epoch       -0.0002966  0.0003547  -0.836    0.404

Residual standard error: 1.696 on 196 degrees of freedom
Multiple R-squared:  0.008483,  Adjusted R-squared:  -0.01175 
F-statistic: 0.4192 on 4 and 196 DF,  p-value: 0.7946

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -0.020811 
Standard error............: 0.133555 
Odds ratio (effect size)..: 0.979 
Lower 95% CI..............: 0.754 
Upper 95% CI..............: 1.272 
T-value...................: -0.155825 
P-value...................: 0.876331 
R^2.......................: 0.008483 
Adjusted r^2..............: -0.011752 
Sample size of AE DB......: 622 
Sample size of model......: 201 
Missing data %............: 67.68489 

- processing TGFB_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   6.7301681    -0.0004431  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6888 -1.1462 -0.3846  0.4296 11.2378 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.0545748  4.0688009   1.980    0.049 *
currentDF[, TRAIT] -0.0552007  0.1221205  -0.452    0.652  
Age                -0.0186150  0.0147203  -1.265    0.207  
Gendermale          0.0317045  0.2741846   0.116    0.908  
ORdate_epoch       -0.0004501  0.0003142  -1.432    0.153  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.813 on 226 degrees of freedom
Multiple R-squared:  0.0166,    Adjusted R-squared:  -0.0008069 
F-statistic: 0.9536 on 4 and 226 DF,  p-value: 0.4338

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: TGFB_rank 
Effect size...............: -0.055201 
Standard error............: 0.12212 
Odds ratio (effect size)..: 0.946 
Lower 95% CI..............: 0.745 
Upper 95% CI..............: 1.202 
T-value...................: -0.452018 
P-value...................: 0.6516892 
R^2.......................: 0.016598 
Adjusted r^2..............: -0.000807 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP2_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   8.1164990    -0.0005594  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7167 -1.1221 -0.4249  0.3331 11.1493 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         9.4867994  4.0618157   2.336   0.0204 *
currentDF[, TRAIT] -0.0850339  0.1207955  -0.704   0.4822  
Age                -0.0193699  0.0143887  -1.346   0.1796  
Gendermale          0.1244951  0.2719640   0.458   0.6476  
ORdate_epoch       -0.0005712  0.0003162  -1.806   0.0722 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.78 on 226 degrees of freedom
Multiple R-squared:  0.02475,   Adjusted R-squared:  0.007486 
F-statistic: 1.434 on 4 and 226 DF,  p-value: 0.2237

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MMP2_rank 
Effect size...............: -0.085034 
Standard error............: 0.120795 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.725 
Upper 95% CI..............: 1.164 
T-value...................: -0.703949 
P-value...................: 0.4821893 
R^2.......................: 0.024747 
Adjusted r^2..............: 0.007486 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP8_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   8.1164990    -0.0005594  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.7527 -1.0843 -0.4417  0.3890 11.3520 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         9.0725255  3.9903143   2.274   0.0239 *
currentDF[, TRAIT]  0.1261784  0.1180766   1.069   0.2864  
Age                -0.0181829  0.0143133  -1.270   0.2053  
Gendermale          0.1218940  0.2699790   0.451   0.6521  
ORdate_epoch       -0.0005445  0.0003120  -1.745   0.0824 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.778 on 226 degrees of freedom
Multiple R-squared:  0.02752,   Adjusted R-squared:  0.01031 
F-statistic: 1.599 on 4 and 226 DF,  p-value: 0.1755

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MMP8_rank 
Effect size...............: 0.126178 
Standard error............: 0.118077 
Odds ratio (effect size)..: 1.134 
Lower 95% CI..............: 0.9 
Upper 95% CI..............: 1.43 
T-value...................: 1.068614 
P-value...................: 0.2863835 
R^2.......................: 0.027523 
Adjusted r^2..............: 0.010311 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP9_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   8.1164990    -0.0005594  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8686 -1.0633 -0.4639  0.4149 11.4019 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)         8.3843517  4.0080256   2.092   0.0376 *
currentDF[, TRAIT]  0.1564731  0.1178386   1.328   0.1856  
Age                -0.0181903  0.0142928  -1.273   0.2044  
Gendermale          0.1564497  0.2679138   0.584   0.5598  
ORdate_epoch       -0.0004915  0.0003134  -1.568   0.1182  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.775 on 226 degrees of freedom
Multiple R-squared:  0.03018,   Adjusted R-squared:  0.01301 
F-statistic: 1.758 on 4 and 226 DF,  p-value: 0.1383

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MMP9_rank 
Effect size...............: 0.156473 
Standard error............: 0.117839 
Odds ratio (effect size)..: 1.169 
Lower 95% CI..............: 0.928 
Upper 95% CI..............: 1.473 
T-value...................: 1.32786 
P-value...................: 0.1855638 
R^2.......................: 0.030175 
Adjusted r^2..............: 0.01301 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

Analysis of COL4A1.

- processing IL2_rank
filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      188.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-213.13 -117.70  -66.53   37.24 1541.83 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -317.05668  628.04850  -0.505    0.614
currentDF[, TRAIT]  -14.45029   17.42979  -0.829    0.408
Age                  -0.53457    2.10405  -0.254    0.800
Gendermale           37.16274   40.81691   0.910    0.364
ORdate_epoch          0.04124    0.04968   0.830    0.408

Residual standard error: 230.3 on 177 degrees of freedom
Multiple R-squared:  0.0158,    Adjusted R-squared:  -0.006447 
F-statistic: 0.7101 on 4 and 177 DF,  p-value: 0.586

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL2_rank 
Effect size...............: -14.45029 
Standard error............: 17.42979 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 363790224 
T-value...................: -0.829057 
P-value...................: 0.4081886 
R^2.......................: 0.015795 
Adjusted r^2..............: -0.006447 
Sample size of AE DB......: 622 
Sample size of model......: 182 
Missing data %............: 70.73955 

- processing IL4_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      200.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-230.56 -132.85  -73.12   25.93 2099.95 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -767.53602  868.35914  -0.884    0.378
currentDF[, TRAIT]  -13.38814   23.51100  -0.569    0.570
Age                  -0.02810    2.81398  -0.010    0.992
Gendermale           56.31212   55.19838   1.020    0.309
ORdate_epoch          0.07475    0.06892   1.085    0.280

Residual standard error: 291.4 on 159 degrees of freedom
Multiple R-squared:  0.02034,   Adjusted R-squared:  -0.004308 
F-statistic: 0.8252 on 4 and 159 DF,  p-value: 0.5109

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL4_rank 
Effect size...............: -13.38814 
Standard error............: 23.511 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.579681e+14 
T-value...................: -0.569442 
P-value...................: 0.56986 
R^2.......................: 0.020337 
Adjusted r^2..............: -0.004308 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL5_rank
filter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            198.66              -34.34  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-244.51 -129.50  -73.56   26.50 2117.59 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -654.85855  789.28468  -0.830    0.408
currentDF[, TRAIT]  -28.78463   21.53674  -1.337    0.183
Age                  -0.30554    2.60248  -0.117    0.907
Gendermale           48.37542   50.57379   0.957    0.340
ORdate_epoch          0.06741    0.06326   1.066    0.288

Residual standard error: 281 on 174 degrees of freedom
Multiple R-squared:  0.02754,   Adjusted R-squared:  0.005185 
F-statistic: 1.232 on 4 and 174 DF,  p-value: 0.2991

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL5_rank 
Effect size...............: -28.78463 
Standard error............: 21.53674 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 678325.2 
T-value...................: -1.336536 
P-value...................: 0.1831192 
R^2.......................: 0.027541 
Adjusted r^2..............: 0.005185 
Sample size of AE DB......: 622 
Sample size of model......: 179 
Missing data %............: 71.22186 

- processing IL6_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      195.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-229.29 -122.84  -76.57   15.18 2132.20 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -621.70357  690.10018  -0.901    0.369
currentDF[, TRAIT]  -17.98881   20.58333  -0.874    0.383
Age                   0.47329    2.49217   0.190    0.850
Gendermale           46.74868   46.44932   1.006    0.316
ORdate_epoch          0.06019    0.05508   1.093    0.276

Residual standard error: 275.3 on 184 degrees of freedom
Multiple R-squared:  0.01806,   Adjusted R-squared:  -0.003285 
F-statistic: 0.8461 on 4 and 184 DF,  p-value: 0.4976

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL6_rank 
Effect size...............: -17.98881 
Standard error............: 20.58333 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5110250474 
T-value...................: -0.873951 
P-value...................: 0.3832844 
R^2.......................: 0.018061 
Adjusted r^2..............: -0.003285 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing IL8_rank
filter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      187.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-215.30 -113.97  -55.21   29.32 2141.67 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -487.70975  650.10915  -0.750    0.454
currentDF[, TRAIT]   12.21431   19.40409   0.629    0.530
Age                  -0.40496    2.22033  -0.182    0.855
Gendermale           27.28702   44.18316   0.618    0.538
ORdate_epoch          0.05467    0.05168   1.058    0.292

Residual standard error: 248.8 on 175 degrees of freedom
Multiple R-squared:  0.01434,   Adjusted R-squared:  -0.008187 
F-statistic: 0.6366 on 4 and 175 DF,  p-value: 0.6371

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL8_rank 
Effect size...............: 12.21431 
Standard error............: 19.40409 
Odds ratio (effect size)..: 201654.2 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.632824e+21 
T-value...................: 0.629471 
P-value...................: 0.5298617 
R^2.......................: 0.014342 
Adjusted r^2..............: -0.008187 
Sample size of AE DB......: 622 
Sample size of model......: 180 
Missing data %............: 71.06109 

- processing IL9_rank
filter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            205.20               39.72  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-335.71 -127.48  -68.12   22.30 2019.38 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -792.08915  651.20572  -1.216   0.2253  
currentDF[, TRAIT]   41.31384   20.02806   2.063   0.0404 *
Age                   0.88111    2.47091   0.357   0.7218  
Gendermale           58.72943   46.04252   1.276   0.2036  
ORdate_epoch          0.07108    0.04986   1.426   0.1555  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 287.4 on 205 degrees of freedom
Multiple R-squared:  0.03564,   Adjusted R-squared:  0.01682 
F-statistic: 1.894 on 4 and 205 DF,  p-value: 0.1128

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL9_rank 
Effect size...............: 41.31384 
Standard error............: 20.02806 
Odds ratio (effect size)..: 8.757322e+17 
Lower 95% CI..............: 7.837 
Upper 95% CI..............: 9.785901e+34 
T-value...................: 2.062798 
P-value...................: 0.04039223 
R^2.......................: 0.035639 
Adjusted r^2..............: 0.016823 
Sample size of AE DB......: 622 
Sample size of model......: 210 
Missing data %............: 66.23794 

- processing IL10_rank
filter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      186.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-204.85 -122.25  -68.75   23.17 1546.21 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -393.48539  769.49394  -0.511    0.610
currentDF[, TRAIT]   -9.14838   20.26396  -0.451    0.652
Age                  -0.45515    2.41117  -0.189    0.851
Gendermale           42.34724   46.72262   0.906    0.366
ORdate_epoch          0.04666    0.06040   0.773    0.441

Residual standard error: 243.5 on 152 degrees of freedom
Multiple R-squared:  0.01375,   Adjusted R-squared:  -0.0122 
F-statistic: 0.5298 on 4 and 152 DF,  p-value: 0.714

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL10_rank 
Effect size...............: -9.148382 
Standard error............: 20.26396 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.887714e+13 
T-value...................: -0.451461 
P-value...................: 0.652301 
R^2.......................: 0.013751 
Adjusted r^2..............: -0.012203 
Sample size of AE DB......: 622 
Sample size of model......: 157 
Missing data %............: 74.75884 

- processing IL12_rank
filter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            205.81              -36.71  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-249.55 -137.32  -76.19   40.86 2077.24 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -554.87789  867.45181  -0.640    0.523
currentDF[, TRAIT]  -31.43446   23.17946  -1.356    0.177
Age                  -0.60123    2.80845  -0.214    0.831
Gendermale           55.96986   54.09862   1.035    0.302
ORdate_epoch          0.06109    0.06867   0.890    0.375

Residual standard error: 290.1 on 161 degrees of freedom
Multiple R-squared:  0.02882,   Adjusted R-squared:  0.004693 
F-statistic: 1.194 on 4 and 161 DF,  p-value: 0.3153

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL12_rank 
Effect size...............: -31.43446 
Standard error............: 23.17946 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1199330 
T-value...................: -1.356135 
P-value...................: 0.1769553 
R^2.......................: 0.028822 
Adjusted r^2..............: 0.004693 
Sample size of AE DB......: 622 
Sample size of model......: 166 
Missing data %............: 73.3119 

- processing IL13_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            205.64               34.02  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-303.18 -124.44  -76.26    9.90 2045.44 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -725.83440  635.95306  -1.141   0.2549  
currentDF[, TRAIT]   32.60273   19.10689   1.706   0.0893 .
Age                   1.67169    2.32723   0.718   0.4733  
Gendermale           43.17562   43.76688   0.986   0.3250  
ORdate_epoch          0.06255    0.04909   1.274   0.2039  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 285.5 on 225 degrees of freedom
Multiple R-squared:  0.02704,   Adjusted R-squared:  0.009739 
F-statistic: 1.563 on 4 and 225 DF,  p-value: 0.1851

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL13_rank 
Effect size...............: 32.60273 
Standard error............: 19.10689 
Odds ratio (effect size)..: 1.44273e+14 
Lower 95% CI..............: 0.008 
Upper 95% CI..............: 2.650307e+30 
T-value...................: 1.706334 
P-value...................: 0.08932573 
R^2.......................: 0.027036 
Adjusted r^2..............: 0.009739 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing IL21_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            205.64               27.33  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-275.74 -127.95  -78.99    9.43 2062.49 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -752.64578  637.00504  -1.182    0.239
currentDF[, TRAIT]   25.65886   19.08899   1.344    0.180
Age                   1.48949    2.32637   0.640    0.523
Gendermale           43.34273   43.91957   0.987    0.325
ORdate_epoch          0.06566    0.04912   1.337    0.183

Residual standard error: 286.2 on 225 degrees of freedom
Multiple R-squared:  0.0223,    Adjusted R-squared:  0.004915 
F-statistic: 1.283 on 4 and 225 DF,  p-value: 0.2776

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL21_rank 
Effect size...............: 25.65886 
Standard error............: 19.08899 
Odds ratio (effect size)..: 139155894574 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.468187e+27 
T-value...................: 1.344171 
P-value...................: 0.1802469 
R^2.......................: 0.022297 
Adjusted r^2..............: 0.004915 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing INFG_rank
filter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      204.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-226.73 -131.07  -72.60   25.29 2110.51 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -685.78217  818.59690  -0.838    0.403
currentDF[, TRAIT]    1.24188   23.61012   0.053    0.958
Age                   0.31646    2.70700   0.117    0.907
Gendermale           53.81032   53.92417   0.998    0.320
ORdate_epoch          0.06647    0.06440   1.032    0.303

Residual standard error: 286.4 on 168 degrees of freedom
Multiple R-squared:  0.01462,   Adjusted R-squared:  -0.008838 
F-statistic: 0.6233 on 4 and 168 DF,  p-value: 0.6465

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: INFG_rank 
Effect size...............: 1.241881 
Standard error............: 23.61012 
Odds ratio (effect size)..: 3.462 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.331971e+20 
T-value...................: 0.0526 
P-value...................: 0.9581135 
R^2.......................: 0.014623 
Adjusted r^2..............: -0.008838 
Sample size of AE DB......: 622 
Sample size of model......: 173 
Missing data %............: 72.18649 

- processing TNFA_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            201.43              -37.62  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-237.45 -135.63  -80.97   49.63 2069.96 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -484.54955  861.51554  -0.562    0.575
currentDF[, TRAIT]  -33.72112   23.04675  -1.463    0.145
Age                   0.02559    2.81822   0.009    0.993
Gendermale           50.23601   53.49291   0.939    0.349
ORdate_epoch          0.05203    0.06793   0.766    0.445

Residual standard error: 290.4 on 159 degrees of freedom
Multiple R-squared:  0.0265,    Adjusted R-squared:  0.002008 
F-statistic: 1.082 on 4 and 159 DF,  p-value: 0.3673

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: TNFA_rank 
Effect size...............: -33.72112 
Standard error............: 23.04675 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 93948.92 
T-value...................: -1.463162 
P-value...................: 0.1453971 
R^2.......................: 0.026498 
Adjusted r^2..............: 0.002008 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing MIF_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      205.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-223.16 -127.42  -77.97   12.67 2127.04 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -930.58713  714.92666  -1.302    0.194
currentDF[, TRAIT]    9.63732   21.31285   0.452    0.652
Age                   1.25346    2.32744   0.539    0.591
Gendermale           48.38055   43.94155   1.101    0.272
ORdate_epoch          0.08080    0.05517   1.465    0.144

Residual standard error: 287.2 on 225 degrees of freedom
Multiple R-squared:  0.01534,   Adjusted R-squared:  -0.002165 
F-statistic: 0.8763 on 4 and 225 DF,  p-value: 0.4788

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MIF_rank 
Effect size...............: 9.637319 
Standard error............: 21.31285 
Odds ratio (effect size)..: 15326.19 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.124704e+22 
T-value...................: 0.452183 
P-value...................: 0.6515724 
R^2.......................: 0.01534 
Adjusted r^2..............: -0.002165 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MCP1_rank
filter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      206.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-247.31 -129.28  -74.16    6.13 2108.99 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -901.73286  655.69621  -1.375    0.170
currentDF[, TRAIT]   17.31190   19.62992   0.882    0.379
Age                   1.61165    2.35908   0.683    0.495
Gendermale           46.58601   44.42933   1.049    0.296
ORdate_epoch          0.07676    0.05039   1.523    0.129

Residual standard error: 287.8 on 223 degrees of freedom
Multiple R-squared:  0.01796,   Adjusted R-squared:  0.0003434 
F-statistic: 1.019 on 4 and 223 DF,  p-value: 0.3981

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MCP1_rank 
Effect size...............: 17.3119 
Standard error............: 19.62992 
Odds ratio (effect size)..: 32996257 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.689609e+24 
T-value...................: 0.881914 
P-value...................: 0.3787727 
R^2.......................: 0.017958 
Adjusted r^2..............: 0.000343 
Sample size of AE DB......: 622 
Sample size of model......: 228 
Missing data %............: 63.34405 

- processing MIP1a_rank
filter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      205.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-273.08 -123.20  -75.68   14.18 2066.46 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -706.69839  649.55398  -1.088    0.278
currentDF[, TRAIT]   22.75281   19.84147   1.147    0.253
Age                   0.86772    2.41348   0.360    0.720
Gendermale           58.42077   46.17518   1.265    0.207
ORdate_epoch          0.06442    0.04991   1.291    0.198

Residual standard error: 288.5 on 209 degrees of freedom
Multiple R-squared:  0.02217,   Adjusted R-squared:  0.00346 
F-statistic: 1.185 on 4 and 209 DF,  p-value: 0.3185

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 22.75281 
Standard error............: 19.84147 
Odds ratio (effect size)..: 7610585913 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.8995e+26 
T-value...................: 1.14673 
P-value...................: 0.2528043 
R^2.......................: 0.022175 
Adjusted r^2..............: 0.00346 
Sample size of AE DB......: 622 
Sample size of model......: 214 
Missing data %............: 65.59485 

- processing RANTES_rank
filter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -1081.0194             35.0248              0.1025  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-266.70 -129.52  -75.71   27.56 2068.67 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.343e+03  7.143e+02  -1.881   0.0613 .
currentDF[, TRAIT]  3.860e+01  2.146e+01   1.799   0.0734 .
Age                 2.086e+00  2.382e+00   0.876   0.3821  
Gendermale          4.866e+01  4.469e+01   1.089   0.2775  
ORdate_epoch        1.092e-01  5.423e-02   2.014   0.0452 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 287.6 on 221 degrees of freedom
Multiple R-squared:  0.02859,   Adjusted R-squared:  0.01101 
F-statistic: 1.626 on 4 and 221 DF,  p-value: 0.1687

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: RANTES_rank 
Effect size...............: 38.5955 
Standard error............: 21.45887 
Odds ratio (effect size)..: 5.778455e+16 
Lower 95% CI..............: 0.031 
Upper 95% CI..............: 1.066521e+35 
T-value...................: 1.79858 
P-value...................: 0.07344962 
R^2.......................: 0.028589 
Adjusted r^2..............: 0.011007 
Sample size of AE DB......: 622 
Sample size of model......: 226 
Missing data %............: 63.6656 

- processing MIG_rank
filter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            205.78               41.42  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-310.04 -127.38  -67.83   14.93 2040.06 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -472.50241  661.04325  -0.715    0.475  
currentDF[, TRAIT]   38.19963   20.28410   1.883    0.061 .
Age                   1.95581    2.35601   0.830    0.407  
Gendermale           42.18063   44.40233   0.950    0.343  
ORdate_epoch          0.04091    0.05157   0.793    0.428  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 286.8 on 222 degrees of freedom
Multiple R-squared:  0.03013,   Adjusted R-squared:  0.01266 
F-statistic: 1.724 on 4 and 222 DF,  p-value: 0.1456

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MIG_rank 
Effect size...............: 38.19963 
Standard error............: 20.2841 
Odds ratio (effect size)..: 3.88945e+16 
Lower 95% CI..............: 0.211 
Upper 95% CI..............: 7.178969e+33 
T-value...................: 1.88323 
P-value...................: 0.06097663 
R^2.......................: 0.030131 
Adjusted r^2..............: 0.012656 
Sample size of AE DB......: 622 
Sample size of model......: 227 
Missing data %............: 63.50482 

- processing IP10_rank
filter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_epoch, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale        ORdate_epoch  
        -755.60678            47.74536            70.08152             0.07247  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-344.89 -129.23  -69.29   18.14 1964.10 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -854.87340  664.82462  -1.286   0.2000  
currentDF[, TRAIT]   49.82612   20.47094   2.434   0.0158 *
Age                   1.46653    2.49915   0.587   0.5580  
Gendermale           70.24809   45.63088   1.539   0.1253  
ORdate_epoch          0.07246    0.05091   1.423   0.1562  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 289.2 on 202 degrees of freedom
Multiple R-squared:  0.04767,   Adjusted R-squared:  0.02881 
F-statistic: 2.528 on 4 and 202 DF,  p-value: 0.04189

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IP10_rank 
Effect size...............: 49.82612 
Standard error............: 20.47094 
Odds ratio (effect size)..: 4.357218e+21 
Lower 95% CI..............: 16368.06 
Upper 95% CI..............: 1.159903e+39 
T-value...................: 2.433993 
P-value...................: 0.01580257 
R^2.......................: 0.047669 
Adjusted r^2..............: 0.028811 
Sample size of AE DB......: 622 
Sample size of model......: 207 
Missing data %............: 66.72026 

- processing Eotaxin1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      205.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-245.04 -127.92  -77.39    7.17 2095.01 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -722.70239  645.19678  -1.120    0.264
currentDF[, TRAIT]   13.01896   19.32326   0.674    0.501
Age                   1.29994    2.32795   0.558    0.577
Gendermale           45.29038   44.10129   1.027    0.306
ORdate_epoch          0.06418    0.04982   1.288    0.199

Residual standard error: 287 on 225 degrees of freedom
Multiple R-squared:  0.01643,   Adjusted R-squared:  -0.001056 
F-statistic: 0.9396 on 4 and 225 DF,  p-value: 0.4418

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 13.01896 
Standard error............: 19.32326 
Odds ratio (effect size)..: 450883.7 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.265773e+22 
T-value...................: 0.673746 
P-value...................: 0.5011645 
R^2.......................: 0.01643 
Adjusted r^2..............: -0.001056 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing TARC_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_epoch, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale        ORdate_epoch  
        -1.047e+03           4.269e+01           7.399e+01           9.568e-02  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-330.95 -142.36  -73.90   17.26 1988.11 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.324e+03  8.482e+02  -1.561   0.1202  
currentDF[, TRAIT]  4.666e+01  2.281e+01   2.046   0.0421 *
Age                 2.891e+00  2.616e+00   1.105   0.2705  
Gendermale          7.736e+01  4.914e+01   1.574   0.1170  
ORdate_epoch        1.019e-01  6.407e-02   1.590   0.1134  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 299.1 on 198 degrees of freedom
Multiple R-squared:  0.03543,   Adjusted R-squared:  0.01594 
F-statistic: 1.818 on 4 and 198 DF,  p-value: 0.1268

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: TARC_rank 
Effect size...............: 46.65927 
Standard error............: 22.81021 
Odds ratio (effect size)..: 1.835967e+20 
Lower 95% CI..............: 7.037 
Upper 95% CI..............: 4.789734e+39 
T-value...................: 2.045543 
P-value...................: 0.04212381 
R^2.......................: 0.035428 
Adjusted r^2..............: 0.015942 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing PARC_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -1.010e+03           3.445e+01           9.675e-02  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-250.57 -133.27  -67.80   23.21 2055.24 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.222e+03  6.765e+02  -1.806   0.0722 .
currentDF[, TRAIT]  3.735e+01  2.013e+01   1.855   0.0649 .
Age                 1.490e+00  2.314e+00   0.644   0.5202  
Gendermale          5.475e+01  4.377e+01   1.251   0.2123  
ORdate_epoch        1.023e-01  5.197e-02   1.969   0.0501 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 285.1 on 225 degrees of freedom
Multiple R-squared:  0.02929,   Adjusted R-squared:  0.01204 
F-statistic: 1.697 on 4 and 225 DF,  p-value: 0.1515

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: PARC_rank 
Effect size...............: 37.34501 
Standard error............: 20.13069 
Odds ratio (effect size)..: 1.654753e+16 
Lower 95% CI..............: 0.121 
Upper 95% CI..............: 2.261118e+33 
T-value...................: 1.855128 
P-value...................: 0.06488597 
R^2.......................: 0.029293 
Adjusted r^2..............: 0.012036 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MDC_rank
filter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      207.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-297.43 -130.77  -82.59   25.49 2047.83 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.074e+03  6.853e+02  -1.568   0.1184  
currentDF[, TRAIT]  2.957e+01  2.085e+01   1.418   0.1576  
Age                 1.171e+00  2.423e+00   0.483   0.6295  
Gendermale          6.975e+01  4.580e+01   1.523   0.1293  
ORdate_epoch        9.153e-02  5.251e-02   1.743   0.0828 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 288.8 on 210 degrees of freedom
Multiple R-squared:  0.02775,   Adjusted R-squared:  0.009231 
F-statistic: 1.498 on 4 and 210 DF,  p-value: 0.2038

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MDC_rank 
Effect size...............: 29.57013 
Standard error............: 20.85122 
Odds ratio (effect size)..: 6.952573e+12 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.900005e+30 
T-value...................: 1.418149 
P-value...................: 0.1576296 
R^2.......................: 0.02775 
Adjusted r^2..............: 0.009231 
Sample size of AE DB......: 622 
Sample size of model......: 215 
Missing data %............: 65.43408 

- processing OPG_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             205.6                27.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-290.59 -130.79  -76.44   15.85 2055.80 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -777.10271  636.20688  -1.221    0.223
currentDF[, TRAIT]   27.39706   19.07990   1.436    0.152
Age                   1.60325    2.33198   0.688    0.492
Gendermale           45.04050   43.80538   1.028    0.305
ORdate_epoch          0.06689    0.04905   1.364    0.174

Residual standard error: 286 on 225 degrees of freedom
Multiple R-squared:  0.02339,   Adjusted R-squared:  0.006033 
F-statistic: 1.347 on 4 and 225 DF,  p-value: 0.2532

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: OPG_rank 
Effect size...............: 27.39706 
Standard error............: 19.0799 
Odds ratio (effect size)..: 791389492757 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.378881e+28 
T-value...................: 1.435912 
P-value...................: 0.1524162 
R^2.......................: 0.023395 
Adjusted r^2..............: 0.006033 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing sICAM1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      205.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-214.09 -129.11  -73.54   10.71 2113.04 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -781.10158  659.56071  -1.184    0.238
currentDF[, TRAIT]   -0.52191   19.68239  -0.027    0.979
Age                   1.19552    2.35059   0.509    0.612
Gendermale           47.99473   43.99985   1.091    0.277
ORdate_epoch          0.06923    0.05046   1.372    0.171

Residual standard error: 287.3 on 225 degrees of freedom
Multiple R-squared:  0.01445,   Adjusted R-squared:  -0.003072 
F-statistic: 0.8246 on 4 and 225 DF,  p-value: 0.5107

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -0.521912 
Standard error............: 19.68239 
Odds ratio (effect size)..: 0.593 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.367608e+16 
T-value...................: -0.026517 
P-value...................: 0.9788687 
R^2.......................: 0.014449 
Adjusted r^2..............: -0.003072 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing VEGFA_rank
filter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      204.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-210.07 -129.65  -71.82    4.26 2112.57 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -696.91443  741.53124  -0.940    0.348
currentDF[, TRAIT]   -1.33360   21.94157  -0.061    0.952
Age                   1.73942    2.43179   0.715    0.475
Gendermale           48.67837   45.36909   1.073    0.285
ORdate_epoch          0.05952    0.05828   1.021    0.308

Residual standard error: 278.6 on 196 degrees of freedom
Multiple R-squared:  0.01309,   Adjusted R-squared:  -0.007054 
F-statistic: 0.6498 on 4 and 196 DF,  p-value: 0.6277

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -1.333605 
Standard error............: 21.94157 
Odds ratio (effect size)..: 0.264 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.252742e+18 
T-value...................: -0.06078 
P-value...................: 0.9515965 
R^2.......................: 0.013087 
Adjusted r^2..............: -0.007054 
Sample size of AE DB......: 622 
Sample size of model......: 201 
Missing data %............: 67.68489 

- processing TGFB_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -667.15046       0.06965  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-221.83 -132.17  -75.20   19.34 2116.15 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -847.45948  643.54080  -1.317    0.189
currentDF[, TRAIT]    4.97623   19.31515   0.258    0.797
Age                   1.53386    2.32823   0.659    0.511
Gendermale           42.23258   43.36633   0.974    0.331
ORdate_epoch          0.07325    0.04970   1.474    0.142

Residual standard error: 286.8 on 226 degrees of freedom
Multiple R-squared:  0.01491,   Adjusted R-squared:  -0.002528 
F-statistic: 0.855 on 4 and 226 DF,  p-value: 0.4918

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: TGFB_rank 
Effect size...............: 4.976229 
Standard error............: 19.31515 
Odds ratio (effect size)..: 144.927 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.004408e+18 
T-value...................: 0.257633 
P-value...................: 0.7969243 
R^2.......................: 0.014907 
Adjusted r^2..............: -0.002528 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP2_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      197.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-207.50 -121.48  -67.92   15.73 2128.45 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -422.58909  600.71406  -0.703    0.482
currentDF[, TRAIT]   -8.59638   17.86481  -0.481    0.631
Age                   1.08386    2.12798   0.509    0.611
Gendermale           35.16087   40.22157   0.874    0.383
ORdate_epoch          0.04152    0.04677   0.888    0.376

Residual standard error: 263.3 on 226 degrees of freedom
Multiple R-squared:  0.0106,    Adjusted R-squared:  -0.006908 
F-statistic: 0.6055 on 4 and 226 DF,  p-value: 0.6591

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MMP2_rank 
Effect size...............: -8.596383 
Standard error............: 17.86481 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 297487232388 
T-value...................: -0.481191 
P-value...................: 0.6308464 
R^2.......................: 0.010603 
Adjusted r^2..............: -0.006908 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP8_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      197.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-223.61 -120.49  -75.25   15.77 2133.39 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -459.17609  589.69775  -0.779    0.437
currentDF[, TRAIT]   19.25183   17.44964   1.103    0.271
Age                   1.21842    2.11525   0.576    0.565
Gendermale           33.23172   39.89811   0.833    0.406
ORdate_epoch          0.04382    0.04611   0.950    0.343

Residual standard error: 262.7 on 226 degrees of freedom
Multiple R-squared:  0.0149,    Adjusted R-squared:  -0.00254 
F-statistic: 0.8543 on 4 and 226 DF,  p-value: 0.4922

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MMP8_rank 
Effect size...............: 19.25183 
Standard error............: 17.44964 
Odds ratio (effect size)..: 229594660 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.638295e+23 
T-value...................: 1.103279 
P-value...................: 0.2710792 
R^2.......................: 0.014895 
Adjusted r^2..............: -0.00254 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP9_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            197.27               28.48  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-228.73 -122.63  -64.32   34.22 2123.20 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -590.74040  590.57612  -1.000   0.3182  
currentDF[, TRAIT]   30.97537   17.36332   1.784   0.0758 .
Age                   1.22980    2.10602   0.584   0.5598  
Gendermale           38.60355   39.47666   0.978   0.3292  
ORdate_epoch          0.05394    0.04618   1.168   0.2440  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 261.6 on 226 degrees of freedom
Multiple R-squared:  0.02334,   Adjusted R-squared:  0.006057 
F-statistic:  1.35 on 4 and 226 DF,  p-value: 0.2522

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MMP9_rank 
Effect size...............: 30.97537 
Standard error............: 17.36332 
Odds ratio (effect size)..: 2.834218e+13 
Lower 95% CI..............: 0.047 
Upper 95% CI..............: 1.707617e+28 
T-value...................: 1.783954 
P-value...................: 0.07577275 
R^2.......................: 0.023343 
Adjusted r^2..............: 0.006057 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

Analysis of COL4A2.

- processing IL2_rank
filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      368.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-461.0 -291.5 -186.1   -5.6 7540.8 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -1945.1003  2089.6614  -0.931    0.353
currentDF[, TRAIT]   -19.3127    57.9929  -0.333    0.740
Age                   -0.1441     7.0007  -0.021    0.984
Gendermale           122.2846   135.8072   0.900    0.369
ORdate_epoch           0.1792     0.1653   1.084    0.280

Residual standard error: 766.3 on 177 degrees of freedom
Multiple R-squared:  0.01418,   Adjusted R-squared:  -0.008097 
F-statistic: 0.6366 on 4 and 177 DF,  p-value: 0.6371

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL2_rank 
Effect size...............: -19.31268 
Standard error............: 57.9929 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.487646e+40 
T-value...................: -0.333018 
P-value...................: 0.7395148 
R^2.......................: 0.014181 
Adjusted r^2..............: -0.008097 
Sample size of AE DB......: 622 
Sample size of model......: 182 
Missing data %............: 70.73955 

- processing IL4_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -3859.8346        0.3449  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-609.2 -347.3 -229.4  -19.8 7860.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3571.8245  3001.7681  -1.190    0.236
currentDF[, TRAIT]   -30.9619    81.2735  -0.381    0.704
Age                    1.4386     9.7275   0.148    0.883
Gendermale           198.1260   190.8113   1.038    0.301
ORdate_epoch           0.3016     0.2382   1.266    0.207

Residual standard error: 1007 on 159 degrees of freedom
Multiple R-squared:  0.02227,   Adjusted R-squared:  -0.002331 
F-statistic: 0.9052 on 4 and 159 DF,  p-value: 0.4624

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL4_rank 
Effect size...............: -30.96185 
Standard error............: 81.27347 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.430201e+55 
T-value...................: -0.380959 
P-value...................: 0.7037423 
R^2.......................: 0.022266 
Adjusted r^2..............: -0.002331 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL5_rank
filter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -3551.8636        0.3195  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-643.0 -334.1 -224.5    4.6 7906.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3119.5274  2741.8148  -1.138    0.257
currentDF[, TRAIT]   -59.2424    74.8143  -0.792    0.430
Age                    0.5915     9.0405   0.065    0.948
Gendermale           167.0915   175.6831   0.951    0.343
ORdate_epoch           0.2712     0.2198   1.234    0.219

Residual standard error: 976.1 on 174 degrees of freedom
Multiple R-squared:  0.02242,   Adjusted R-squared:  -4.937e-05 
F-statistic: 0.9978 on 4 and 174 DF,  p-value: 0.4103

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL5_rank 
Effect size...............: -59.24243 
Standard error............: 74.81427 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.006101e+37 
T-value...................: -0.79186 
P-value...................: 0.4295205 
R^2.......................: 0.022424 
Adjusted r^2..............: -4.9e-05 
Sample size of AE DB......: 622 
Sample size of model......: 179 
Missing data %............: 71.22186 

- processing IL6_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      403.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-518.8 -329.3 -227.5  -45.9 7934.5 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2253.2474  2379.9327  -0.947    0.345
currentDF[, TRAIT]    -6.3536    70.9853  -0.090    0.929
Age                    2.4920     8.5947   0.290    0.772
Gendermale           166.1146   160.1887   1.037    0.301
ORdate_epoch           0.1897     0.1899   0.999    0.319

Residual standard error: 949.4 on 184 degrees of freedom
Multiple R-squared:  0.01304,   Adjusted R-squared:  -0.008413 
F-statistic: 0.6079 on 4 and 184 DF,  p-value: 0.6575

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL6_rank 
Effect size...............: -6.353606 
Standard error............: 70.98526 
Odds ratio (effect size)..: 0.002 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.61887e+57 
T-value...................: -0.089506 
P-value...................: 0.9287771 
R^2.......................: 0.013042 
Adjusted r^2..............: -0.008413 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing IL8_rank
filter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2486.1323        0.2281  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-508.4 -286.0 -163.6    6.5 8013.7 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2474.2315  2020.5508  -1.225    0.222
currentDF[, TRAIT]   -19.0319    60.3082  -0.316    0.753
Age                   -4.0754     6.9008  -0.591    0.556
Gendermale           101.8304   137.3221   0.742    0.459
ORdate_epoch           0.2430     0.1606   1.513    0.132

Residual standard error: 773.2 on 175 degrees of freedom
Multiple R-squared:  0.01725,   Adjusted R-squared:  -0.005212 
F-statistic: 0.768 on 4 and 175 DF,  p-value: 0.5474

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL8_rank 
Effect size...............: -19.03186 
Standard error............: 60.30824 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.174851e+43 
T-value...................: -0.315576 
P-value...................: 0.7527001 
R^2.......................: 0.017251 
Adjusted r^2..............: -0.005212 
Sample size of AE DB......: 622 
Sample size of model......: 180 
Missing data %............: 71.06109 

- processing IL9_rank
filter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      425.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-770.8 -350.9 -216.3  -14.5 7718.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3192.8936  2252.4646  -1.418    0.158
currentDF[, TRAIT]    85.8878    69.2753   1.240    0.216
Age                    4.6972     8.5467   0.550    0.583
Gendermale           189.2008   159.2571   1.188    0.236
ORdate_epoch           0.2513     0.1725   1.457    0.147

Residual standard error: 994 on 205 degrees of freedom
Multiple R-squared:  0.02363,   Adjusted R-squared:  0.004582 
F-statistic: 1.241 on 4 and 205 DF,  p-value: 0.2949

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL9_rank 
Effect size...............: 85.88781 
Standard error............: 69.27534 
Odds ratio (effect size)..: 1.998037e+37 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.85765e+96 
T-value...................: 1.239804 
P-value...................: 0.2164654 
R^2.......................: 0.023634 
Adjusted r^2..............: 0.004582 
Sample size of AE DB......: 622 
Sample size of model......: 210 
Missing data %............: 66.23794 

- processing IL10_rank
filter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      364.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-507.6 -294.5 -182.4  -30.0 7527.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2.495e+03  2.545e+03  -0.980    0.328
currentDF[, TRAIT]  4.006e+00  6.702e+01   0.060    0.952
Age                 5.101e-02  7.975e+00   0.006    0.995
Gendermale          1.494e+02  1.545e+02   0.967    0.335
ORdate_epoch        2.213e-01  1.998e-01   1.108    0.270

Residual standard error: 805.3 on 152 degrees of freedom
Multiple R-squared:  0.01592,   Adjusted R-squared:  -0.009979 
F-statistic: 0.6147 on 4 and 152 DF,  p-value: 0.6527

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL10_rank 
Effect size...............: 4.006071 
Standard error............: 67.01937 
Odds ratio (effect size)..: 54.931 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.135592e+58 
T-value...................: 0.059775 
P-value...................: 0.9524135 
R^2.......................: 0.015918 
Adjusted r^2..............: -0.009979 
Sample size of AE DB......: 622 
Sample size of model......: 157 
Missing data %............: 74.75884 

- processing IL12_rank
filter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -3663.1511        0.3295  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-632.5 -370.0 -227.5   11.1 7805.1 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2802.6425  3017.9267  -0.929    0.354
currentDF[, TRAIT]   -85.3166    80.6430  -1.058    0.292
Age                   -0.5392     9.7708  -0.055    0.956
Gendermale           187.2526   188.2130   0.995    0.321
ORdate_epoch           0.2517     0.2389   1.053    0.294

Residual standard error: 1009 on 161 degrees of freedom
Multiple R-squared:  0.02587,   Adjusted R-squared:  0.001667 
F-statistic: 1.069 on 4 and 161 DF,  p-value: 0.3738

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL12_rank 
Effect size...............: -85.31664 
Standard error............: 80.64298 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.909721e+31 
T-value...................: -1.057955 
P-value...................: 0.2916607 
R^2.......................: 0.025869 
Adjusted r^2..............: 0.001667 
Sample size of AE DB......: 622 
Sample size of model......: 166 
Missing data %............: 73.3119 

- processing IL13_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            425.29               98.14  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-780.5 -346.9 -218.9  -47.9 7717.5 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3080.5475  2171.4739  -1.419    0.157
currentDF[, TRAIT]    91.8409    65.2408   1.408    0.161
Age                    5.9940     7.9464   0.754    0.451
Gendermale           187.5593   149.4428   1.255    0.211
ORdate_epoch           0.2355     0.1676   1.405    0.161

Residual standard error: 974.8 on 225 degrees of freedom
Multiple R-squared:  0.02712,   Adjusted R-squared:  0.009826 
F-statistic: 1.568 on 4 and 225 DF,  p-value: 0.1837

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL13_rank 
Effect size...............: 91.84085 
Standard error............: 65.24084 
Odds ratio (effect size)..: 7.69086e+39 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.630844e+95 
T-value...................: 1.40772 
P-value...................: 0.160594 
R^2.......................: 0.027122 
Adjusted r^2..............: 0.009826 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing IL21_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2634.8462        0.2436  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-678.8 -347.2 -223.3  -28.3 7786.2 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3169.4111  2174.9753  -1.457    0.146
currentDF[, TRAIT]    61.8405    65.1770   0.949    0.344
Age                    5.3648     7.9431   0.675    0.500
Gendermale           189.9441   149.9580   1.267    0.207
ORdate_epoch           0.2459     0.1677   1.466    0.144

Residual standard error: 977.1 on 225 degrees of freedom
Multiple R-squared:  0.02246,   Adjusted R-squared:  0.005086 
F-statistic: 1.293 on 4 and 225 DF,  p-value: 0.2738

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL21_rank 
Effect size...............: 61.84047 
Standard error............: 65.17701 
Odds ratio (effect size)..: 7.194045e+26 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.171506e+82 
T-value...................: 0.948808 
P-value...................: 0.3437362 
R^2.......................: 0.022464 
Adjusted r^2..............: 0.005086 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing INFG_rank
filter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      419.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-611.8 -350.8 -222.4  -60.2 7900.9 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3270.2779  2843.4237  -1.150    0.252
currentDF[, TRAIT]    30.4820    82.0106   0.372    0.711
Age                    3.3330     9.4029   0.354    0.723
Gendermale           192.2499   187.3074   1.026    0.306
ORdate_epoch           0.2665     0.2237   1.191    0.235

Residual standard error: 994.7 on 168 degrees of freedom
Multiple R-squared:  0.01677,   Adjusted R-squared:  -0.006636 
F-statistic: 0.7165 on 4 and 168 DF,  p-value: 0.5817

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: INFG_rank 
Effect size...............: 30.48197 
Standard error............: 82.01055 
Odds ratio (effect size)..: 1.730413e+13 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.11415e+83 
T-value...................: 0.371683 
P-value...................: 0.7105969 
R^2.......................: 0.016774 
Adjusted r^2..............: -0.006636 
Sample size of AE DB......: 622 
Sample size of model......: 173 
Missing data %............: 72.18649 

- processing TNFA_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      420.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-661.3 -366.2 -238.0    9.6 7785.8 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2824.3152  2988.9771  -0.945    0.346
currentDF[, TRAIT]   -83.7855    79.9593  -1.048    0.296
Age                    1.5029     9.7776   0.154    0.878
Gendermale           182.6437   185.5905   0.984    0.327
ORdate_epoch           0.2419     0.2357   1.026    0.306

Residual standard error: 1008 on 159 degrees of freedom
Multiple R-squared:  0.02372,   Adjusted R-squared:  -0.0008397 
F-statistic: 0.9658 on 4 and 159 DF,  p-value: 0.428

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: TNFA_rank 
Effect size...............: -83.78554 
Standard error............: 79.95933 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.733214e+31 
T-value...................: -1.047852 
P-value...................: 0.296297 
R^2.......................: 0.023721 
Adjusted r^2..............: -0.00084 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing MIF_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2634.8462        0.2436  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-560.8 -349.2 -226.6   -8.3 7889.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3052.3167  2437.0785  -1.252    0.212
currentDF[, TRAIT]   -13.0189    72.6523  -0.179    0.858
Age                    4.6119     7.9339   0.581    0.562
Gendermale           200.8322   149.7902   1.341    0.181
ORdate_epoch           0.2400     0.1881   1.276    0.203

Residual standard error: 979 on 225 degrees of freedom
Multiple R-squared:  0.01869,   Adjusted R-squared:  0.001248 
F-statistic: 1.072 on 4 and 225 DF,  p-value: 0.3714

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MIF_rank 
Effect size...............: -13.01887 
Standard error............: 72.65233 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.54486e+56 
T-value...................: -0.179194 
P-value...................: 0.8579465 
R^2.......................: 0.018693 
Adjusted r^2..............: 0.001248 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MCP1_rank
filter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2593.9993        0.2406  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-574.9 -349.0 -229.9   -6.0 7904.8 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3198.9288  2239.7333  -1.428    0.155
currentDF[, TRAIT]    -6.0970    67.0521  -0.091    0.928
Age                    5.0368     8.0582   0.625    0.533
Gendermale           204.0845   151.7621   1.345    0.180
ORdate_epoch           0.2493     0.1721   1.448    0.149

Residual standard error: 983 on 223 degrees of freedom
Multiple R-squared:  0.01862,   Adjusted R-squared:  0.001013 
F-statistic: 1.058 on 4 and 223 DF,  p-value: 0.3784

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MCP1_rank 
Effect size...............: -6.097011 
Standard error............: 67.05205 
Odds ratio (effect size)..: 0.002 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.678922e+54 
T-value...................: -0.09093 
P-value...................: 0.9276302 
R^2.......................: 0.018616 
Adjusted r^2..............: 0.001013 
Sample size of AE DB......: 622 
Sample size of model......: 228 
Missing data %............: 63.34405 

- processing MIP1a_rank
filter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      433.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-603.4 -344.6 -236.3  -22.3 7841.6 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2975.2993  2263.2006  -1.315    0.190
currentDF[, TRAIT]    29.1841    69.1324   0.422    0.673
Age                    5.0826     8.4091   0.604    0.546
Gendermale           200.0214   160.8853   1.243    0.215
ORdate_epoch           0.2319     0.1739   1.333    0.184

Residual standard error: 1005 on 209 degrees of freedom
Multiple R-squared:  0.01766,   Adjusted R-squared:  -0.001136 
F-statistic: 0.9396 on 4 and 209 DF,  p-value: 0.4419

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 29.1841 
Standard error............: 69.13239 
Odds ratio (effect size)..: 4.726008e+12 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.320252e+71 
T-value...................: 0.422148 
P-value...................: 0.6733509 
R^2.......................: 0.017665 
Adjusted r^2..............: -0.001136 
Sample size of AE DB......: 622 
Sample size of model......: 214 
Missing data %............: 65.59485 

- processing RANTES_rank
filter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2570.6277        0.2388  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-660.1 -356.9 -220.9    8.3 7798.0 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -4596.7225  2442.8131  -1.882   0.0612 .
currentDF[, TRAIT]    95.0634    73.3891   1.295   0.1966  
Age                    6.8155     8.1449   0.837   0.4036  
Gendermale           201.8777   152.8473   1.321   0.1879  
ORdate_epoch           0.3512     0.1855   1.893   0.0596 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 983.7 on 221 degrees of freedom
Multiple R-squared:  0.02553,   Adjusted R-squared:  0.007895 
F-statistic: 1.448 on 4 and 221 DF,  p-value: 0.2193

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: RANTES_rank 
Effect size...............: 95.06344 
Standard error............: 73.38914 
Odds ratio (effect size)..: 1.929866e+41 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.696677e+103 
T-value...................: 1.295334 
P-value...................: 0.1965565 
R^2.......................: 0.025533 
Adjusted r^2..............: 0.007895 
Sample size of AE DB......: 622 
Sample size of model......: 226 
Missing data %............: 63.6656 

- processing MIG_rank
filter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             427.4               110.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-766.3 -343.3 -215.5  -17.8 7727.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2460.1144  2262.0638  -1.088    0.278
currentDF[, TRAIT]    93.8105    69.4114   1.352    0.178
Age                    6.5666     8.0622   0.814    0.416
Gendermale           185.5187   151.9430   1.221    0.223
ORdate_epoch           0.1833     0.1765   1.039    0.300

Residual standard error: 981.4 on 222 degrees of freedom
Multiple R-squared:  0.02645,   Adjusted R-squared:  0.008904 
F-statistic: 1.508 on 4 and 222 DF,  p-value: 0.2009

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MIG_rank 
Effect size...............: 93.8105 
Standard error............: 69.41138 
Odds ratio (effect size)..: 5.512936e+40 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.691845e+99 
T-value...................: 1.351515 
P-value...................: 0.1779062 
R^2.......................: 0.026445 
Adjusted r^2..............: 0.008904 
Sample size of AE DB......: 622 
Sample size of model......: 227 
Missing data %............: 63.50482 

- processing IP10_rank
filter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_epoch, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale        ORdate_epoch  
        -2928.7520            102.5032            232.7368              0.2545  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-825.8 -373.5 -210.4  -22.4 7555.3 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3404.8038  2330.4974  -1.461    0.146
currentDF[, TRAIT]   112.4819    71.7595   1.567    0.119
Age                    7.0330     8.7606   0.803    0.423
Gendermale           233.5356   159.9559   1.460    0.146
ORdate_epoch           0.2544     0.1785   1.426    0.155

Residual standard error: 1014 on 202 degrees of freedom
Multiple R-squared:  0.0319,    Adjusted R-squared:  0.01273 
F-statistic: 1.664 on 4 and 202 DF,  p-value: 0.1597

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IP10_rank 
Effect size...............: 112.4819 
Standard error............: 71.75947 
Odds ratio (effect size)..: 7.083509e+48 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.573218e+109 
T-value...................: 1.567484 
P-value...................: 0.1185667 
R^2.......................: 0.031901 
Adjusted r^2..............: 0.01273 
Sample size of AE DB......: 622 
Sample size of model......: 207 
Missing data %............: 66.72026 

- processing Eotaxin1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2634.8462        0.2436  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-597.2 -344.0 -232.0  -10.9 7875.7 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3136.0606  2200.1422  -1.425    0.155
currentDF[, TRAIT]    23.3202    65.8930   0.354    0.724
Age                    4.8489     7.9384   0.611    0.542
Gendermale           196.3442   150.3869   1.306    0.193
ORdate_epoch           0.2456     0.1699   1.446    0.150

Residual standard error: 978.8 on 225 degrees of freedom
Multiple R-squared:  0.0191,    Adjusted R-squared:  0.001661 
F-statistic: 1.095 on 4 and 225 DF,  p-value: 0.3597

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 23.32025 
Standard error............: 65.89295 
Odds ratio (effect size)..: 13423141327 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.648422e+66 
T-value...................: 0.353911 
P-value...................: 0.7237369 
R^2.......................: 0.019099 
Adjusted r^2..............: 0.001661 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing TARC_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_epoch, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale        ORdate_epoch  
        -3773.0185            114.3868            263.6155              0.3198  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-847.8 -400.3 -212.1  -12.6 7574.0 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -4569.3095  2925.0326  -1.562    0.120
currentDF[, TRAIT]   125.7896    78.6581   1.599    0.111
Age                    8.3111     9.0200   0.921    0.358
Gendermale           273.2925   169.4428   1.613    0.108
ORdate_epoch           0.3376     0.2209   1.528    0.128

Residual standard error: 1032 on 198 degrees of freedom
Multiple R-squared:  0.02888,   Adjusted R-squared:  0.009263 
F-statistic: 1.472 on 4 and 198 DF,  p-value: 0.212

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: TARC_rank 
Effect size...............: 125.7896 
Standard error............: 78.65813 
Odds ratio (effect size)..: 4.263086e+54 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.84485e+121 
T-value...................: 1.599194 
P-value...................: 0.1113721 
R^2.......................: 0.028882 
Adjusted r^2..............: 0.009263 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing PARC_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2634.8462        0.2436  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-641.1 -345.8 -226.9  -17.4 7776.0 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -4245.2846  2314.9359  -1.834   0.0680 .
currentDF[, TRAIT]    85.2705    68.8847   1.238   0.2171  
Age                    5.3302     7.9166   0.673   0.5015  
Gendermale           216.5889   149.7836   1.446   0.1496  
ORdate_epoch           0.3301     0.1778   1.856   0.0647 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 975.7 on 225 degrees of freedom
Multiple R-squared:  0.02519,   Adjusted R-squared:  0.007862 
F-statistic: 1.454 on 4 and 225 DF,  p-value: 0.2173

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: PARC_rank 
Effect size...............: 85.27054 
Standard error............: 68.88471 
Odds ratio (effect size)..: 1.077769e+37 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.65991e+95 
T-value...................: 1.237873 
P-value...................: 0.2170532 
R^2.......................: 0.025192 
Adjusted r^2..............: 0.007862 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MDC_rank
filter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
        442  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-619.2 -355.7 -242.4   -0.7 7834.5 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3425.4247  2396.2086  -1.430    0.154
currentDF[, TRAIT]    24.6877    72.9094   0.339    0.735
Age                    5.6913     8.4719   0.672    0.502
Gendermale           223.5451   160.1485   1.396    0.164
ORdate_epoch           0.2637     0.1836   1.436    0.152

Residual standard error: 1010 on 210 degrees of freedom
Multiple R-squared:  0.01967,   Adjusted R-squared:  0.0009959 
F-statistic: 1.053 on 4 and 210 DF,  p-value: 0.3807

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MDC_rank 
Effect size...............: 24.68774 
Standard error............: 72.90941 
Odds ratio (effect size)..: 52692592722 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.074237e+72 
T-value...................: 0.338608 
P-value...................: 0.735243 
R^2.......................: 0.019669 
Adjusted r^2..............: 0.000996 
Sample size of AE DB......: 622 
Sample size of model......: 215 
Missing data %............: 65.43408 

- processing OPG_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -2525.1721            103.3467              0.2349  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-822.3 -343.3 -212.9  -33.5 7687.6 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3216.3921  2165.1723  -1.486    0.139
currentDF[, TRAIT]   105.4097    64.9337   1.623    0.106
Age                    6.2121     7.9363   0.783    0.435
Gendermale           189.7141   149.0807   1.273    0.204
ORdate_epoch           0.2450     0.1669   1.468    0.143

Residual standard error: 973.4 on 225 degrees of freedom
Multiple R-squared:  0.02991,   Adjusted R-squared:  0.01267 
F-statistic: 1.735 on 4 and 225 DF,  p-value: 0.1433

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: OPG_rank 
Effect size...............: 105.4097 
Standard error............: 64.9337 
Odds ratio (effect size)..: 6.009686e+45 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.125979e+101 
T-value...................: 1.623344 
P-value...................: 0.1059162 
R^2.......................: 0.029915 
Adjusted r^2..............: 0.012669 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing sICAM1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2634.8462        0.2436  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-627.6 -354.7 -237.8   23.0 7897.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2684.1732  2242.3307  -1.197    0.233
currentDF[, TRAIT]   -68.1091    66.9149  -1.018    0.310
Age                    3.5039     7.9914   0.438    0.661
Gendermale           194.4293   149.5878   1.300    0.195
ORdate_epoch           0.2170     0.1715   1.265    0.207

Residual standard error: 976.8 on 225 degrees of freedom
Multiple R-squared:  0.02305,   Adjusted R-squared:  0.005684 
F-statistic: 1.327 on 4 and 225 DF,  p-value: 0.2606

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -68.10911 
Standard error............: 66.91487 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.397033e+27 
T-value...................: -1.017847 
P-value...................: 0.3098438 
R^2.......................: 0.023051 
Adjusted r^2..............: 0.005684 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing VEGFA_rank
filter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      411.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-580.6 -336.9 -215.9  -25.2 7971.1 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2906.4481  2483.2679  -1.170    0.243
currentDF[, TRAIT]   -53.0233    73.4787  -0.722    0.471
Age                    2.2669     8.1437   0.278    0.781
Gendermale           199.6167   151.9337   1.314    0.190
ORdate_epoch           0.2401     0.1952   1.230    0.220

Residual standard error: 932.9 on 196 degrees of freedom
Multiple R-squared:  0.01528,   Adjusted R-squared:  -0.004816 
F-statistic: 0.7603 on 4 and 196 DF,  p-value: 0.5523

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -53.02325 
Standard error............: 73.47875 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.30117e+39 
T-value...................: -0.721613 
P-value...................: 0.4713921 
R^2.......................: 0.01528 
Adjusted r^2..............: -0.004816 
Sample size of AE DB......: 622 
Sample size of model......: 201 
Missing data %............: 67.68489 

- processing TGFB_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -2670.9926        0.2468  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-582.8 -351.6 -228.6  -12.2 7900.6 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -3255.4089  2191.7975  -1.485    0.139
currentDF[, TRAIT]    -1.5566    65.7843  -0.024    0.981
Age                    4.9552     7.9296   0.625    0.533
Gendermale           196.0778   147.6988   1.328    0.186
ORdate_epoch           0.2550     0.1693   1.507    0.133

Residual standard error: 976.9 on 226 degrees of freedom
Multiple R-squared:  0.01905,   Adjusted R-squared:  0.001685 
F-statistic: 1.097 on 4 and 226 DF,  p-value: 0.3588

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: TGFB_rank 
Effect size...............: -1.556598 
Standard error............: 65.78433 
Odds ratio (effect size)..: 0.211 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.09283e+55 
T-value...................: -0.023662 
P-value...................: 0.981143 
R^2.......................: 0.019047 
Adjusted r^2..............: 0.001685 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP2_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      396.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-501.3 -317.0 -208.8  -25.7 7945.2 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2050.3944  2013.2917  -1.018    0.310
currentDF[, TRAIT]     5.5043    59.8739   0.092    0.927
Age                    3.3422     7.1319   0.469    0.640
Gendermale           171.0026   134.8025   1.269    0.206
ORdate_epoch           0.1671     0.1568   1.066    0.288

Residual standard error: 882.3 on 226 degrees of freedom
Multiple R-squared:  0.01293,   Adjusted R-squared:  -0.004539 
F-statistic: 0.7402 on 4 and 226 DF,  p-value: 0.5655

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MMP2_rank 
Effect size...............: 5.504269 
Standard error............: 59.87385 
Odds ratio (effect size)..: 245.739 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.270536e+53 
T-value...................: 0.091931 
P-value...................: 0.9268342 
R^2.......................: 0.012932 
Adjusted r^2..............: -0.004539 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP8_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      396.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-506.7 -321.5 -205.6  -19.7 7938.7 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2032.3328  1980.2522  -1.026    0.306
currentDF[, TRAIT]   -18.9107    58.5973  -0.323    0.747
Age                    3.2413     7.1032   0.456    0.649
Gendermale           173.9266   133.9810   1.298    0.196
ORdate_epoch           0.1660     0.1549   1.072    0.285

Residual standard error: 882.1 on 226 degrees of freedom
Multiple R-squared:  0.01335,   Adjusted R-squared:  -0.004113 
F-statistic: 0.7644 on 4 and 226 DF,  p-value: 0.5494

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MMP8_rank 
Effect size...............: -18.91074 
Standard error............: 58.59728 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.63635e+41 
T-value...................: -0.322724 
P-value...................: 0.7472027 
R^2.......................: 0.013349 
Adjusted r^2..............: -0.004113 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP9_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      396.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-503.8 -315.7 -210.8  -25.5 7948.1 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -2085.3780  1991.7887  -1.047    0.296
currentDF[, TRAIT]    18.2990    58.5599   0.312    0.755
Age                    3.3159     7.1028   0.467    0.641
Gendermale           169.3323   133.1398   1.272    0.205
ORdate_epoch           0.1701     0.1557   1.092    0.276

Residual standard error: 882.1 on 226 degrees of freedom
Multiple R-squared:  0.01332,   Adjusted R-squared:  -0.004142 
F-statistic: 0.7628 on 4 and 226 DF,  p-value: 0.5505

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MMP9_rank 
Effect size...............: 18.29904 
Standard error............: 58.55988 
Odds ratio (effect size)..: 88547027 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.228007e+57 
T-value...................: 0.312484 
P-value...................: 0.7549606 
R^2.......................: 0.013321 
Adjusted r^2..............: -0.004142 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

Analysis of LDLR.

- processing IL2_rank
filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      232.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-252.3 -136.2  -69.5   25.9 3233.9 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         74.95626  871.41591   0.086    0.932
currentDF[, TRAIT] -27.23453   24.18379  -1.126    0.262
Age                 -0.99300    2.91937  -0.340    0.734
Gendermale          35.88049   56.63337   0.634    0.527
ORdate_epoch         0.01590    0.06893   0.231    0.818

Residual standard error: 319.6 on 177 degrees of freedom
Multiple R-squared:  0.01195,   Adjusted R-squared:  -0.01038 
F-statistic: 0.5353 on 4 and 177 DF,  p-value: 0.71

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL2_rank 
Effect size...............: -27.23454 
Standard error............: 24.18379 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 572597648 
T-value...................: -1.126148 
P-value...................: 0.2616273 
R^2.......................: 0.011953 
Adjusted r^2..............: -0.010376 
Sample size of AE DB......: 622 
Sample size of model......: 182 
Missing data %............: 70.73955 

- processing IL4_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      232.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-238.5 -146.5  -68.5   19.3 3255.9 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -109.46381 1000.61883  -0.109    0.913
currentDF[, TRAIT]  -11.73571   27.09196  -0.433    0.665
Age                  -1.05572    3.24258  -0.326    0.745
Gendermale           49.59202   63.60564   0.780    0.437
ORdate_epoch          0.03027    0.07942   0.381    0.704

Residual standard error: 335.7 on 159 degrees of freedom
Multiple R-squared:  0.007739,  Adjusted R-squared:  -0.01722 
F-statistic:  0.31 on 4 and 159 DF,  p-value: 0.871

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL4_rank 
Effect size...............: -11.73571 
Standard error............: 27.09196 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.212675e+17 
T-value...................: -0.43318 
P-value...................: 0.6654707 
R^2.......................: 0.007739 
Adjusted r^2..............: -0.017224 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL5_rank
filter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             228.4               -39.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-260.4 -136.8  -64.0   28.5 3218.6 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         61.97559  899.99742   0.069    0.945
currentDF[, TRAIT] -37.99062   24.55770  -1.547    0.124
Age                 -1.44835    2.96753  -0.488    0.626
Gendermale          40.75431   57.66776   0.707    0.481
ORdate_epoch         0.01879    0.07214   0.260    0.795

Residual standard error: 320.4 on 174 degrees of freedom
Multiple R-squared:  0.01986,   Adjusted R-squared:  -0.002668 
F-statistic: 0.8816 on 4 and 174 DF,  p-value: 0.4762

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL5_rank 
Effect size...............: -37.99062 
Standard error............: 24.55769 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 25398.87 
T-value...................: -1.546995 
P-value...................: 0.1236814 
R^2.......................: 0.019864 
Adjusted r^2..............: -0.002668 
Sample size of AE DB......: 622 
Sample size of model......: 179 
Missing data %............: 71.22186 

- processing IL6_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             228.5               -41.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-294.8 -135.9  -61.2   29.3 3200.5 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -254.51799  784.26410  -0.325    0.746  
currentDF[, TRAIT]  -41.88768   23.39192  -1.791    0.075 .
Age                  -1.38282    2.83222  -0.488    0.626  
Gendermale           31.36037   52.78732   0.594    0.553  
ORdate_epoch          0.04441    0.06259   0.709    0.479  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 312.8 on 184 degrees of freedom
Multiple R-squared:  0.02331,   Adjusted R-squared:  0.002074 
F-statistic: 1.098 on 4 and 184 DF,  p-value: 0.3592

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL6_rank 
Effect size...............: -41.88768 
Standard error............: 23.39192 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 52.482 
T-value...................: -1.79069 
P-value...................: 0.07498695 
R^2.......................: 0.023306 
Adjusted r^2..............: 0.002074 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing IL8_rank
filter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            237.52               37.91  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-261.4 -145.0  -57.4   49.5 3239.1 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        275.307244 839.704655   0.328    0.743
currentDF[, TRAIT]  37.066695  25.063024   1.479    0.141
Age                 -0.108796   2.867861  -0.038    0.970
Gendermale          25.027043  57.068584   0.439    0.662
ORdate_epoch        -0.003977   0.066748  -0.060    0.953

Residual standard error: 321.3 on 175 degrees of freedom
Multiple R-squared:  0.01511,   Adjusted R-squared:  -0.007406 
F-statistic: 0.671 on 4 and 175 DF,  p-value: 0.6129

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL8_rank 
Effect size...............: 37.0667 
Standard error............: 25.06302 
Odds ratio (effect size)..: 1.25274e+16 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.703569e+37 
T-value...................: 1.478939 
P-value...................: 0.1409543 
R^2.......................: 0.015106 
Adjusted r^2..............: -0.007406 
Sample size of AE DB......: 622 
Sample size of model......: 180 
Missing data %............: 71.06109 

- processing IL9_rank
filter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             238.1                43.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-354.8 -136.5  -65.9   21.7 3203.8 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -310.61239  694.23303  -0.447   0.6550  
currentDF[, TRAIT]   43.63537   21.35138   2.044   0.0423 *
Age                  -0.30010    2.63417  -0.114   0.9094  
Gendermale           47.80791   49.08471   0.974   0.3312  
ORdate_epoch          0.04240    0.05315   0.798   0.4259  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 306.4 on 205 degrees of freedom
Multiple R-squared:  0.02714,   Adjusted R-squared:  0.008156 
F-statistic:  1.43 on 4 and 205 DF,  p-value: 0.2254

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL9_rank 
Effect size...............: 43.63537 
Standard error............: 21.35138 
Odds ratio (effect size)..: 8.924799e+18 
Lower 95% CI..............: 5.969 
Upper 95% CI..............: 1.334322e+37 
T-value...................: 2.043679 
P-value...................: 0.04226425 
R^2.......................: 0.027138 
Adjusted r^2..............: 0.008156 
Sample size of AE DB......: 622 
Sample size of model......: 210 
Missing data %............: 66.23794 

- processing IL10_rank
filter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      230.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-235.9 -150.1  -72.8   16.1 3239.2 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)          61.59955 1081.26283   0.057    0.955
currentDF[, TRAIT]  -21.27182   28.47412  -0.747    0.456
Age                  -0.85351    3.38808  -0.252    0.801
Gendermale           45.48144   65.65281   0.693    0.490
ORdate_epoch          0.01550    0.08487   0.183    0.855

Residual standard error: 342.1 on 152 degrees of freedom
Multiple R-squared:  0.008988,  Adjusted R-squared:  -0.01709 
F-statistic: 0.3446 on 4 and 152 DF,  p-value: 0.8474

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL10_rank 
Effect size...............: -21.27182 
Standard error............: 28.47412 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.986761e+14 
T-value...................: -0.747058 
P-value...................: 0.4561825 
R^2.......................: 0.008988 
Adjusted r^2..............: -0.017091 
Sample size of AE DB......: 622 
Sample size of model......: 157 
Missing data %............: 74.75884 

- processing IL12_rank
filter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      235.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-246.0 -144.9  -69.7   48.2 3234.2 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         52.04500  994.18011   0.052    0.958
currentDF[, TRAIT] -21.34594   26.56580  -0.804    0.423
Age                 -1.15725    3.21875  -0.360    0.720
Gendermale          47.84764   62.00203   0.772    0.441
ORdate_epoch         0.01819    0.07870   0.231    0.817

Residual standard error: 332.5 on 161 degrees of freedom
Multiple R-squared:  0.009725,  Adjusted R-squared:  -0.01488 
F-statistic: 0.3953 on 4 and 161 DF,  p-value: 0.8118

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL12_rank 
Effect size...............: -21.34594 
Standard error............: 26.5658 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.202143e+13 
T-value...................: -0.803512 
P-value...................: 0.4228636 
R^2.......................: 0.009725 
Adjusted r^2..............: -0.014878 
Sample size of AE DB......: 622 
Sample size of model......: 166 
Missing data %............: 73.3119 

- processing IL13_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-285.7 -139.4  -71.6   34.5 3232.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -222.29490  703.29669  -0.316    0.752
currentDF[, TRAIT]   23.71734   21.13019   1.122    0.263
Age                   0.92072    2.57367   0.358    0.721
Gendermale           17.75161   48.40153   0.367    0.714
ORdate_epoch          0.03079    0.05429   0.567    0.571

Residual standard error: 315.7 on 225 degrees of freedom
Multiple R-squared:  0.008443,  Adjusted R-squared:  -0.009185 
F-statistic: 0.479 on 4 and 225 DF,  p-value: 0.7512

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL13_rank 
Effect size...............: 23.71734 
Standard error............: 21.13019 
Odds ratio (effect size)..: 19966909475 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.93506e+28 
T-value...................: 1.122438 
P-value...................: 0.2628724 
R^2.......................: 0.008443 
Adjusted r^2..............: -0.009185 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing IL21_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-278.9 -139.0  -71.9   34.8 3234.0 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -234.61656  702.64078  -0.334    0.739
currentDF[, TRAIT]   24.28831   21.05588   1.154    0.250
Age                   0.85061    2.56608   0.331    0.741
Gendermale           16.84232   48.44496   0.348    0.728
ORdate_epoch          0.03220    0.05419   0.594    0.553

Residual standard error: 315.7 on 225 degrees of freedom
Multiple R-squared:  0.008753,  Adjusted R-squared:  -0.008869 
F-statistic: 0.4967 on 4 and 225 DF,  p-value: 0.7382

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL21_rank 
Effect size...............: 24.28831 
Standard error............: 21.05588 
Odds ratio (effect size)..: 35341101619 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.960796e+28 
T-value...................: 1.153517 
P-value...................: 0.2499222 
R^2.......................: 0.008753 
Adjusted r^2..............: -0.008869 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing INFG_rank
filter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      237.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-238.1 -143.7  -72.2   34.9 3257.7 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)        -56.02598  936.57885  -0.060    0.952
currentDF[, TRAIT]  -4.82446   27.01298  -0.179    0.858
Age                 -1.27016    3.09715  -0.410    0.682
Gendermale          44.88360   61.69610   0.727    0.468
ORdate_epoch         0.02773    0.07368   0.376    0.707

Residual standard error: 327.6 on 168 degrees of freedom
Multiple R-squared:  0.006139,  Adjusted R-squared:  -0.01752 
F-statistic: 0.2594 on 4 and 168 DF,  p-value: 0.9036

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: INFG_rank 
Effect size...............: -4.824462 
Standard error............: 27.01298 
Odds ratio (effect size)..: 0.008 
Lower 95% CI..............: 0 
Upper 95% CI..............: 7.919121e+20 
T-value...................: -0.178598 
P-value...................: 0.8584686 
R^2.......................: 0.006139 
Adjusted r^2..............: -0.017524 
Sample size of AE DB......: 622 
Sample size of model......: 173 
Missing data %............: 72.18649 

- processing TNFA_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      232.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-245.8 -148.2  -70.9   31.9 3229.0 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        171.612984 993.514059   0.173    0.863
currentDF[, TRAIT] -26.408125  26.577894  -0.994    0.322
Age                 -1.300014   3.250018  -0.400    0.690
Gendermale          44.784778  61.688911   0.726    0.469
ORdate_epoch         0.009309   0.078342   0.119    0.906

Residual standard error: 334.9 on 159 degrees of freedom
Multiple R-squared:  0.01126,   Adjusted R-squared:  -0.01361 
F-statistic: 0.4528 on 4 and 159 DF,  p-value: 0.7702

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: TNFA_rank 
Effect size...............: -26.40812 
Standard error............: 26.57789 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 142776613871 
T-value...................: -0.993612 
P-value...................: 0.3219213 
R^2.......................: 0.011263 
Adjusted r^2..............: -0.013611 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing MIF_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-252.2 -138.4  -64.9   39.3 3205.9 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -753.67740  784.78307  -0.960    0.338
currentDF[, TRAIT]   32.40054   23.39536   1.385    0.167
Age                   0.74542    2.55486   0.292    0.771
Gendermale           22.41611   48.23514   0.465    0.643
ORdate_epoch          0.07376    0.06056   1.218    0.224

Residual standard error: 315.2 on 225 degrees of freedom
Multiple R-squared:  0.01132,   Adjusted R-squared:  -0.006258 
F-statistic: 0.644 on 4 and 225 DF,  p-value: 0.6317

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MIF_rank 
Effect size...............: 32.40054 
Standard error............: 23.39536 
Odds ratio (effect size)..: 1.178623e+14 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.680641e+33 
T-value...................: 1.384913 
P-value...................: 0.1674507 
R^2.......................: 0.011319 
Adjusted r^2..............: -0.006258 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MCP1_rank
filter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             241.3                29.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-284.2 -136.6  -62.4   19.8 3231.8 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -499.18417  719.87579  -0.693    0.489
currentDF[, TRAIT]   33.95320   21.55129   1.575    0.117
Age                   1.18539    2.58999   0.458    0.648
Gendermale           17.88480   48.77808   0.367    0.714
ORdate_epoch          0.05148    0.05532   0.931    0.353

Residual standard error: 315.9 on 223 degrees of freedom
Multiple R-squared:  0.01394,   Adjusted R-squared:  -0.003746 
F-statistic: 0.7882 on 4 and 223 DF,  p-value: 0.5339

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MCP1_rank 
Effect size...............: 33.9532 
Standard error............: 21.55129 
Odds ratio (effect size)..: 5.567856e+14 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.231739e+33 
T-value...................: 1.57546 
P-value...................: 0.1165679 
R^2.......................: 0.013941 
Adjusted r^2..............: -0.003746 
Sample size of AE DB......: 622 
Sample size of model......: 228 
Missing data %............: 63.34405 

- processing MIP1a_rank
filter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            237.19               32.28  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-303.5 -133.0  -59.9   25.0 3233.2 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -223.39329  687.23876  -0.325    0.745
currentDF[, TRAIT]   30.69566   20.99260   1.462    0.145
Age                  -0.41957    2.55350  -0.164    0.870
Gendermale           44.68521   48.85410   0.915    0.361
ORdate_epoch          0.03621    0.05281   0.686    0.494

Residual standard error: 305.3 on 209 degrees of freedom
Multiple R-squared:  0.01707,   Adjusted R-squared:  -0.001739 
F-statistic: 0.9075 on 4 and 209 DF,  p-value: 0.4604

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 30.69566 
Standard error............: 20.9926 
Odds ratio (effect size)..: 2.142678e+13 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.585681e+31 
T-value...................: 1.462214 
P-value...................: 0.1451847 
R^2.......................: 0.017073 
Adjusted r^2..............: -0.001739 
Sample size of AE DB......: 622 
Sample size of model......: 214 
Missing data %............: 65.59485 

- processing RANTES_rank
filter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-248.9 -144.7  -70.4   22.9 3260.3 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -566.25323  791.57888  -0.715    0.475
currentDF[, TRAIT]   20.17020   23.78131   0.848    0.397
Age                   1.06379    2.63930   0.403    0.687
Gendermale           22.52060   49.52925   0.455    0.650
ORdate_epoch          0.05713    0.06010   0.951    0.343

Residual standard error: 318.8 on 221 degrees of freedom
Multiple R-squared:  0.006251,  Adjusted R-squared:  -0.01174 
F-statistic: 0.3475 on 4 and 221 DF,  p-value: 0.8456

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: RANTES_rank 
Effect size...............: 20.1702 
Standard error............: 23.78131 
Odds ratio (effect size)..: 575183172 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.006617e+29 
T-value...................: 0.848153 
P-value...................: 0.3972702 
R^2.......................: 0.006251 
Adjusted r^2..............: -0.011736 
Sample size of AE DB......: 622 
Sample size of model......: 226 
Missing data %............: 63.6656 

- processing MIG_rank
filter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            239.26               38.66  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-317.9 -138.4  -61.4   30.3 3230.9 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)        5.004e+01  7.280e+02   0.069    0.945  
currentDF[, TRAIT] 3.916e+01  2.234e+01   1.753    0.081 .
Age                1.319e+00  2.595e+00   0.508    0.612  
Gendermale         1.461e+01  4.890e+01   0.299    0.765  
ORdate_epoch       7.073e-03  5.679e-02   0.125    0.901  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 315.8 on 222 degrees of freedom
Multiple R-squared:  0.0166,    Adjusted R-squared:  -0.001118 
F-statistic: 0.9369 on 4 and 222 DF,  p-value: 0.4433

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MIG_rank 
Effect size...............: 39.15692 
Standard error............: 22.33913 
Odds ratio (effect size)..: 1.013061e+17 
Lower 95% CI..............: 0.01 
Upper 95% CI..............: 1.049755e+36 
T-value...................: 1.75284 
P-value...................: 0.08100985 
R^2.......................: 0.016601 
Adjusted r^2..............: -0.001118 
Sample size of AE DB......: 622 
Sample size of model......: 227 
Missing data %............: 63.50482 

- processing IP10_rank
filter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            236.65               36.44  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-322.9 -135.3  -58.1   23.4 3236.5 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -376.89831  711.07176  -0.530    0.597
currentDF[, TRAIT]   36.04478   21.89495   1.646    0.101
Age                  -0.12584    2.67300  -0.047    0.962
Gendermale           56.69334   48.80510   1.162    0.247
ORdate_epoch          0.04623    0.05445   0.849    0.397

Residual standard error: 309.3 on 202 degrees of freedom
Multiple R-squared:  0.02317,   Adjusted R-squared:  0.003827 
F-statistic: 1.198 on 4 and 202 DF,  p-value: 0.313

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IP10_rank 
Effect size...............: 36.04478 
Standard error............: 21.89495 
Odds ratio (effect size)..: 4.508673e+15 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 1.956184e+34 
T-value...................: 1.64626 
P-value...................: 0.1012652 
R^2.......................: 0.02317 
Adjusted r^2..............: 0.003827 
Sample size of AE DB......: 622 
Sample size of model......: 207 
Missing data %............: 66.72026 

- processing Eotaxin1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-268.0 -140.0  -66.7   31.0 3247.9 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -167.48409  710.19919  -0.236    0.814
currentDF[, TRAIT]   20.37463   21.27004   0.958    0.339
Age                   0.73021    2.56248   0.285    0.776
Gendermale           16.98106   48.54442   0.350    0.727
ORdate_epoch          0.02750    0.05484   0.501    0.617

Residual standard error: 315.9 on 225 degrees of freedom
Multiple R-squared:  0.006941,  Adjusted R-squared:  -0.01071 
F-statistic: 0.3931 on 4 and 225 DF,  p-value: 0.8135

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 20.37463 
Standard error............: 21.27004 
Odds ratio (effect size)..: 705651252 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.99533e+26 
T-value...................: 0.957903 
P-value...................: 0.33914 
R^2.......................: 0.006941 
Adjusted r^2..............: -0.010714 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing TARC_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      246.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-309.7 -132.2  -73.6   20.8 3231.1 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -684.42514  938.36026  -0.729    0.467
currentDF[, TRAIT]   36.38124   25.23379   1.442    0.151
Age                   2.05783    2.89365   0.711    0.478
Gendermale           42.84827   54.35782   0.788    0.431
ORdate_epoch          0.06013    0.07088   0.848    0.397

Residual standard error: 330.9 on 198 degrees of freedom
Multiple R-squared:  0.01398,   Adjusted R-squared:  -0.00594 
F-statistic: 0.7018 on 4 and 198 DF,  p-value: 0.5916

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: TARC_rank 
Effect size...............: 36.38124 
Standard error............: 25.23379 
Odds ratio (effect size)..: 6.312078e+15 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.903752e+37 
T-value...................: 1.441767 
P-value...................: 0.150948 
R^2.......................: 0.01398 
Adjusted r^2..............: -0.00594 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing PARC_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-246.9 -133.7  -69.6   34.3 3212.3 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -577.60233  748.72980  -0.771    0.441
currentDF[, TRAIT]   26.68424   22.27968   1.198    0.232
Age                   0.78495    2.56049   0.307    0.759
Gendermale           26.08549   48.44517   0.538    0.591
ORdate_epoch          0.05931    0.05751   1.031    0.304

Residual standard error: 315.6 on 225 degrees of freedom
Multiple R-squared:  0.009208,  Adjusted R-squared:  -0.008407 
F-statistic: 0.5227 on 4 and 225 DF,  p-value: 0.7191

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: PARC_rank 
Effect size...............: 26.68424 
Standard error............: 22.27968 
Odds ratio (effect size)..: 387986757915 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.578209e+30 
T-value...................: 1.197694 
P-value...................: 0.2322964 
R^2.......................: 0.009208 
Adjusted r^2..............: -0.008407 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MDC_rank
filter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            236.52               31.03  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-330.4 -133.0  -62.5   40.2 3203.4 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -654.99360  721.24872  -0.908   0.3648  
currentDF[, TRAIT]   39.58326   21.94543   1.804   0.0727 .
Age                  -0.20771    2.54999  -0.081   0.9352  
Gendermale           55.01229   48.20403   1.141   0.2551  
ORdate_epoch          0.06875    0.05527   1.244   0.2149  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 303.9 on 210 degrees of freedom
Multiple R-squared:  0.02284,   Adjusted R-squared:  0.004231 
F-statistic: 1.227 on 4 and 210 DF,  p-value: 0.3003

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MDC_rank 
Effect size...............: 39.58326 
Standard error............: 21.94543 
Odds ratio (effect size)..: 1.551636e+17 
Lower 95% CI..............: 0.032 
Upper 95% CI..............: 7.43213e+35 
T-value...................: 1.803713 
P-value...................: 0.07270942 
R^2.......................: 0.022844 
Adjusted r^2..............: 0.004231 
Sample size of AE DB......: 622 
Sample size of model......: 215 
Missing data %............: 65.43408 

- processing OPG_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-236.2 -139.4  -67.6   25.7 3266.3 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -268.38618  703.94227  -0.381    0.703
currentDF[, TRAIT]   -9.02309   21.11129  -0.427    0.669
Age                   0.44954    2.58026   0.174    0.862
Gendermale           22.28581   48.46923   0.460    0.646
ORdate_epoch          0.03673    0.05427   0.677    0.499

Residual standard error: 316.5 on 225 degrees of freedom
Multiple R-squared:  0.0037,    Adjusted R-squared:  -0.01401 
F-statistic: 0.2089 on 4 and 225 DF,  p-value: 0.9333

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: OPG_rank 
Effect size...............: -9.023088 
Standard error............: 21.11129 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.126194e+14 
T-value...................: -0.427406 
P-value...................: 0.6694925 
R^2.......................: 0.0037 
Adjusted r^2..............: -0.014012 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing sICAM1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      240.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-292.8 -141.1  -71.9   30.4 3234.8 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -460.58071  724.85389  -0.635    0.526
currentDF[, TRAIT]   23.53057   21.63084   1.088    0.278
Age                   0.98648    2.58328   0.382    0.703
Gendermale           23.66320   48.35561   0.489    0.625
ORdate_epoch          0.04905    0.05545   0.885    0.377

Residual standard error: 315.8 on 225 degrees of freedom
Multiple R-squared:  0.008108,  Adjusted R-squared:  -0.009526 
F-statistic: 0.4598 on 4 and 225 DF,  p-value: 0.7652

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 23.53057 
Standard error............: 21.63084 
Odds ratio (effect size)..: 16565204929 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.28294e+28 
T-value...................: 1.087825 
P-value...................: 0.2778363 
R^2.......................: 0.008108 
Adjusted r^2..............: -0.009526 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing VEGFA_rank
filter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      243.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-240.1 -145.3  -72.1   30.4 3260.7 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -273.02825  884.59455  -0.309    0.758
currentDF[, TRAIT]    3.96145   26.17474   0.151    0.880
Age                   1.61071    2.90096   0.555    0.579
Gendermale           16.99322   54.12213   0.314    0.754
ORdate_epoch          0.03145    0.06952   0.452    0.651

Residual standard error: 332.3 on 196 degrees of freedom
Multiple R-squared:  0.003598,  Adjusted R-squared:  -0.01674 
F-statistic: 0.1769 on 4 and 196 DF,  p-value: 0.9501

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: VEGFA_rank 
Effect size...............: 3.961448 
Standard error............: 26.17474 
Odds ratio (effect size)..: 52.533 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.001905e+24 
T-value...................: 0.151346 
P-value...................: 0.8798583 
R^2.......................: 0.003598 
Adjusted r^2..............: -0.016737 
Sample size of AE DB......: 622 
Sample size of model......: 201 
Missing data %............: 67.68489 

- processing TGFB_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      243.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-243.5 -136.5  -71.5   30.9 3259.1 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -332.15825  709.47779  -0.468    0.640
currentDF[, TRAIT]    7.41869   21.29418   0.348    0.728
Age                   0.94209    2.56678   0.367    0.714
Gendermale           12.56489   47.80964   0.263    0.793
ORdate_epoch          0.04001    0.05479   0.730    0.466

Residual standard error: 316.2 on 226 degrees of freedom
Multiple R-squared:  0.003353,  Adjusted R-squared:  -0.01429 
F-statistic: 0.1901 on 4 and 226 DF,  p-value: 0.9434

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: TGFB_rank 
Effect size...............: 7.418691 
Standard error............: 21.29418 
Odds ratio (effect size)..: 1666.85 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.227745e+21 
T-value...................: 0.348391 
P-value...................: 0.7278712 
R^2.......................: 0.003353 
Adjusted r^2..............: -0.014286 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP2_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      235.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-265.6 -140.3  -59.5   44.7 3258.7 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         66.90759  709.64445   0.094    0.925
currentDF[, TRAIT] -24.85362   21.10432  -1.178    0.240
Age                  0.39686    2.51386   0.158    0.875
Gendermale           9.85885   47.51514   0.207    0.836
ORdate_epoch         0.01069    0.05525   0.194    0.847

Residual standard error: 311 on 226 degrees of freedom
Multiple R-squared:  0.00773,   Adjusted R-squared:  -0.009832 
F-statistic: 0.4402 on 4 and 226 DF,  p-value: 0.7795

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MMP2_rank 
Effect size...............: -24.85362 
Standard error............: 21.10432 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 14810435 
T-value...................: -1.177656 
P-value...................: 0.2401725 
R^2.......................: 0.00773 
Adjusted r^2..............: -0.009832 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP8_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            235.16               47.97  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-300.2 -132.6  -59.7   50.1 3246.9 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -45.40284  692.04620  -0.066   0.9477  
currentDF[, TRAIT]  47.64552   20.47821   2.327   0.0209 *
Age                  0.76794    2.48237   0.309   0.7573  
Gendermale           6.33712   46.82286   0.135   0.8925  
ORdate_epoch         0.01786    0.05412   0.330   0.7417  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 308.3 on 226 degrees of freedom
Multiple R-squared:  0.02499,   Adjusted R-squared:  0.007738 
F-statistic: 1.448 on 4 and 226 DF,  p-value: 0.219

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MMP8_rank 
Effect size...............: 47.64552 
Standard error............: 20.47821 
Odds ratio (effect size)..: 4.922508e+20 
Lower 95% CI..............: 1822.979 
Upper 95% CI..............: 1.329202e+38 
T-value...................: 2.326645 
P-value...................: 0.02086943 
R^2.......................: 0.024995 
Adjusted r^2..............: 0.007738 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP9_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            235.16               50.18  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-279.9 -137.5  -60.2   36.1 3222.2 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -278.05859  694.53100  -0.400   0.6893  
currentDF[, TRAIT]   51.81355   20.41966   2.537   0.0118 *
Age                   0.75234    2.47673   0.304   0.7616  
Gendermale           19.28365   46.42546   0.415   0.6783  
ORdate_epoch          0.03574    0.05430   0.658   0.5111  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 307.6 on 226 degrees of freedom
Multiple R-squared:  0.0293,    Adjusted R-squared:  0.01211 
F-statistic: 1.705 on 4 and 226 DF,  p-value: 0.1497

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MMP9_rank 
Effect size...............: 51.81355 
Standard error............: 20.41966 
Odds ratio (effect size)..: 3.179361e+22 
Lower 95% CI..............: 132060.5 
Upper 95% CI..............: 7.654322e+39 
T-value...................: 2.537434 
P-value...................: 0.0118407 
R^2.......................: 0.029296 
Adjusted r^2..............: 0.012115 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

Analysis of CD36.

- processing IL2_rank
filter: removed 440 rows (71%), 182 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -843.06851       0.07936  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-190.22 -105.31  -51.46   19.81  945.27 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -872.01033  510.21791  -1.709   0.0892 .
currentDF[, TRAIT]   -5.92588   14.15972  -0.419   0.6761  
Age                   0.95185    1.70930   0.557   0.5783  
Gendermale           -3.03543   33.15909  -0.092   0.9272  
ORdate_epoch          0.07668    0.04036   1.900   0.0591 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 187.1 on 177 degrees of freedom
Multiple R-squared:  0.0246,    Adjusted R-squared:  0.00256 
F-statistic: 1.116 on 4 and 177 DF,  p-value: 0.3505

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL2_rank 
Effect size...............: -5.925881 
Standard error............: 14.15972 
Odds ratio (effect size)..: 0.003 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3015900123 
T-value...................: -0.418503 
P-value...................: 0.6760869 
R^2.......................: 0.024603 
Adjusted r^2..............: 0.00256 
Sample size of AE DB......: 622 
Sample size of model......: 182 
Missing data %............: 70.73955 

- processing IL4_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -1.048e+03     9.619e-02  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-196.14 -113.68  -56.58   14.42  949.91 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.136e+03  5.802e+02  -1.958   0.0519 .
currentDF[, TRAIT]  5.007e+00  1.571e+01   0.319   0.7503  
Age                 1.196e+00  1.880e+00   0.636   0.5255  
Gendermale          1.036e+00  3.688e+01   0.028   0.9776  
ORdate_epoch        9.670e-02  4.605e-02   2.100   0.0373 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.7 on 159 degrees of freedom
Multiple R-squared:  0.03023,   Adjusted R-squared:  0.00583 
F-statistic: 1.239 on 4 and 159 DF,  p-value: 0.2966

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL4_rank 
Effect size...............: 5.006842 
Standard error............: 15.70842 
Odds ratio (effect size)..: 149.432 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.513341e+15 
T-value...................: 0.318736 
P-value...................: 0.7503449 
R^2.......................: 0.030227 
Adjusted r^2..............: 0.00583 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL5_rank
filter: removed 443 rows (71%), 179 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -858.24959       0.08047  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-168.37 -106.75  -52.96   17.97  946.03 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -866.88521  529.30355  -1.638   0.1033  
currentDF[, TRAIT]   -5.43009   14.44279  -0.376   0.7074  
Age                   0.84584    1.74525   0.485   0.6285  
Gendermale           -0.45921   33.91538  -0.014   0.9892  
ORdate_epoch          0.07658    0.04242   1.805   0.0728 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 188.4 on 174 degrees of freedom
Multiple R-squared:  0.02322,   Adjusted R-squared:  0.0007702 
F-statistic: 1.034 on 4 and 174 DF,  p-value: 0.391

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL5_rank 
Effect size...............: -5.430086 
Standard error............: 14.44279 
Odds ratio (effect size)..: 0.004 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8623750207 
T-value...................: -0.375972 
P-value...................: 0.7073957 
R^2.......................: 0.023225 
Adjusted r^2..............: 0.00077 
Sample size of AE DB......: 622 
Sample size of model......: 179 
Missing data %............: 71.22186 

- processing IL6_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -716.78685       0.06837  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-161.67  -95.79  -47.31   16.25  950.68 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -756.51217  445.29336  -1.699   0.0910 .
currentDF[, TRAIT]   -7.90356   13.28158  -0.595   0.5525  
Age                   0.21878    1.60809   0.136   0.8919  
Gendermale           -2.03296   29.97184  -0.068   0.9460  
ORdate_epoch          0.07049    0.03554   1.983   0.0488 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 177.6 on 184 degrees of freedom
Multiple R-squared:  0.02227,   Adjusted R-squared:  0.001011 
F-statistic: 1.048 on 4 and 184 DF,  p-value: 0.3841

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL6_rank 
Effect size...............: -7.903557 
Standard error............: 13.28158 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 74651244 
T-value...................: -0.595077 
P-value...................: 0.5525234 
R^2.......................: 0.022266 
Adjusted r^2..............: 0.001011 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing IL8_rank
filter: removed 442 rows (71%), 180 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            145.01               30.18  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-188.48 -103.67  -47.33   32.35  935.66 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -530.65268  478.79178  -1.108   0.2692  
currentDF[, TRAIT]   26.37301   14.29070   1.845   0.0667 .
Age                   1.37192    1.63523   0.839   0.4026  
Gendermale           -5.45550   32.53998  -0.168   0.8670  
ORdate_epoch          0.04705    0.03806   1.236   0.2180  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 183.2 on 175 degrees of freedom
Multiple R-squared:  0.03995,   Adjusted R-squared:  0.01801 
F-statistic: 1.821 on 4 and 175 DF,  p-value: 0.1269

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL8_rank 
Effect size...............: 26.37301 
Standard error............: 14.2907 
Odds ratio (effect size)..: 284218568638 
Lower 95% CI..............: 0.195 
Upper 95% CI..............: 4.150919e+23 
T-value...................: 1.845466 
P-value...................: 0.06665945 
R^2.......................: 0.03995 
Adjusted r^2..............: 0.018006 
Sample size of AE DB......: 622 
Sample size of model......: 180 
Missing data %............: 71.06109 

- processing IL9_rank
filter: removed 412 rows (66%), 210 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -696.00249       0.06765  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-213.95 -116.05  -58.99   21.66 1530.33 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -718.49466  483.27497  -1.487   0.1386  
currentDF[, TRAIT]   14.63176   14.86329   0.984   0.3261  
Age                  -0.39158    1.83372  -0.214   0.8311  
Gendermale           14.03084   34.16923   0.411   0.6818  
ORdate_epoch          0.07071    0.03700   1.911   0.0574 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 213.3 on 205 degrees of freedom
Multiple R-squared:  0.02205,   Adjusted R-squared:  0.002967 
F-statistic: 1.156 on 4 and 205 DF,  p-value: 0.3317

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL9_rank 
Effect size...............: 14.63176 
Standard error............: 14.86329 
Odds ratio (effect size)..: 2261996 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.014801e+19 
T-value...................: 0.984422 
P-value...................: 0.3260683 
R^2.......................: 0.022049 
Adjusted r^2..............: 0.002967 
Sample size of AE DB......: 622 
Sample size of model......: 210 
Missing data %............: 66.23794 

- processing IL10_rank
filter: removed 465 rows (75%), 157 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -977.29361       0.09042  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-182.16 -114.45  -55.79   11.15  950.36 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.072e+03  6.234e+02  -1.719   0.0876 .
currentDF[, TRAIT]  1.320e+00  1.642e+01   0.080   0.9360  
Age                 1.173e+00  1.953e+00   0.600   0.5491  
Gendermale         -4.255e+00  3.785e+01  -0.112   0.9106  
ORdate_epoch        9.189e-02  4.893e-02   1.878   0.0623 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 197.2 on 152 degrees of freedom
Multiple R-squared:  0.02518,   Adjusted R-squared:  -0.0004686 
F-statistic: 0.9817 on 4 and 152 DF,  p-value: 0.4194

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL10_rank 
Effect size...............: 1.320174 
Standard error............: 16.41548 
Odds ratio (effect size)..: 3.744 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.519493e+14 
T-value...................: 0.080423 
P-value...................: 0.936007 
R^2.......................: 0.025184 
Adjusted r^2..............: -0.000469 
Sample size of AE DB......: 622 
Sample size of model......: 157 
Missing data %............: 74.75884 

- processing IL12_rank
filter: removed 456 rows (73%), 166 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -882.60266       0.08273  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-166.15 -108.05  -54.61   14.28  948.35 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -906.32110  574.44389  -1.578   0.1166  
currentDF[, TRAIT]   -2.78021   15.34990  -0.181   0.8565  
Age                   0.58054    1.85981   0.312   0.7553  
Gendermale           -6.69042   35.82519  -0.187   0.8521  
ORdate_epoch          0.08188    0.04548   1.800   0.0737 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 192.1 on 161 degrees of freedom
Multiple R-squared:  0.0221,    Adjusted R-squared:  -0.002193 
F-statistic: 0.9097 on 4 and 161 DF,  p-value: 0.4598

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL12_rank 
Effect size...............: -2.780212 
Standard error............: 15.3499 
Odds ratio (effect size)..: 0.062 
Lower 95% CI..............: 0 
Upper 95% CI..............: 722215605801 
T-value...................: -0.181122 
P-value...................: 0.8564993 
R^2.......................: 0.022102 
Adjusted r^2..............: -0.002193 
Sample size of AE DB......: 622 
Sample size of model......: 166 
Missing data %............: 73.3119 

- processing IL13_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -736.7299        0.0708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-188.20 -110.92  -63.29   14.33 1524.76 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -739.17742  467.81276  -1.580   0.1155  
currentDF[, TRAIT]    7.03748   14.05520   0.501   0.6171  
Age                   0.10224    1.71193   0.060   0.9524  
Gendermale            8.67745   32.19531   0.270   0.7878  
ORdate_epoch          0.06993    0.03611   1.936   0.0541 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210 on 225 degrees of freedom
Multiple R-squared:  0.01844,   Adjusted R-squared:  0.0009909 
F-statistic: 1.057 on 4 and 225 DF,  p-value: 0.3788

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL13_rank 
Effect size...............: 7.037476 
Standard error............: 14.0552 
Odds ratio (effect size)..: 1138.51 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.048004e+15 
T-value...................: 0.500703 
P-value...................: 0.6170698 
R^2.......................: 0.018441 
Adjusted r^2..............: 0.000991 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing IL21_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -736.7299        0.0708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-178.63 -111.30  -63.74   14.84 1521.85 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -746.95375  467.62598  -1.597   0.1116  
currentDF[, TRAIT]    3.98171   14.01324   0.284   0.7766  
Age                   0.04562    1.70779   0.027   0.9787  
Gendermale            8.99898   32.24140   0.279   0.7804  
ORdate_epoch          0.07083    0.03606   1.964   0.0507 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared:  0.0177,    Adjusted R-squared:  0.0002366 
F-statistic: 1.014 on 4 and 225 DF,  p-value: 0.4012

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL21_rank 
Effect size...............: 3.981713 
Standard error............: 14.01324 
Odds ratio (effect size)..: 53.609 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.545178e+13 
T-value...................: 0.284139 
P-value...................: 0.776565 
R^2.......................: 0.0177 
Adjusted r^2..............: 0.000237 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing INFG_rank
filter: removed 449 rows (72%), 173 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -920.70803       0.08597  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-195.67 -110.38  -56.05   25.21  956.57 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.106e+03  5.430e+02  -2.036   0.0433 *
currentDF[, TRAIT]  1.058e+01  1.566e+01   0.675   0.5005  
Age                 1.019e+00  1.796e+00   0.568   0.5710  
Gendermale         -2.742e+00  3.577e+01  -0.077   0.9390  
ORdate_epoch        9.547e-02  4.272e-02   2.235   0.0267 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 190 on 168 degrees of freedom
Multiple R-squared:  0.03089,   Adjusted R-squared:  0.007817 
F-statistic: 1.339 on 4 and 168 DF,  p-value: 0.2576

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: INFG_rank 
Effect size...............: 10.57548 
Standard error............: 15.66176 
Odds ratio (effect size)..: 39162.83 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.402981e+17 
T-value...................: 0.675242 
P-value...................: 0.5004501 
R^2.......................: 0.030891 
Adjusted r^2..............: 0.007817 
Sample size of AE DB......: 622 
Sample size of model......: 173 
Missing data %............: 72.18649 

- processing TNFA_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -874.51825       0.08224  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-189.45 -111.89  -58.74   19.38  930.67 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -926.27234  575.57164  -1.609   0.1095  
currentDF[, TRAIT]  -11.90293   15.39735  -0.773   0.4406  
Age                   1.35587    1.88283   0.720   0.4725  
Gendermale           -1.89535   35.73818  -0.053   0.9578  
ORdate_epoch          0.07911    0.04539   1.743   0.0832 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194 on 159 degrees of freedom
Multiple R-squared:  0.02777,   Adjusted R-squared:  0.003316 
F-statistic: 1.136 on 4 and 159 DF,  p-value: 0.3418

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: TNFA_rank 
Effect size...............: -11.90293 
Standard error............: 15.39735 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 86518714 
T-value...................: -0.773051 
P-value...................: 0.4406401 
R^2.......................: 0.027775 
Adjusted r^2..............: 0.003316 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing MIF_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -736.7299        0.0708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-182.76 -109.23  -59.91   14.87 1501.78 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -939.15310  522.32673  -1.798   0.0735 .
currentDF[, TRAIT]   12.42244   15.57121   0.798   0.4258  
Age                   0.06449    1.70043   0.038   0.9698  
Gendermale           10.15864   32.10378   0.316   0.7520  
ORdate_epoch          0.08596    0.04031   2.133   0.0340 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 209.8 on 225 degrees of freedom
Multiple R-squared:  0.02012,   Adjusted R-squared:  0.002699 
F-statistic: 1.155 on 4 and 225 DF,  p-value: 0.3316

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MIF_rank 
Effect size...............: 12.42244 
Standard error............: 15.57121 
Odds ratio (effect size)..: 248311.7 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.461454e+18 
T-value...................: 0.797783 
P-value...................: 0.4258378 
R^2.......................: 0.020119 
Adjusted r^2..............: 0.002699 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MCP1_rank
filter: removed 394 rows (63%), 228 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -721.33089       0.06964  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-184.88 -111.64  -64.80   15.03 1511.87 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -804.85997  480.08454  -1.676   0.0950 .
currentDF[, TRAIT]    8.59175   14.37254   0.598   0.5506  
Age                   0.22598    1.72726   0.131   0.8960  
Gendermale            8.39038   32.53006   0.258   0.7967  
ORdate_epoch          0.07456    0.03689   2.021   0.0445 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210.7 on 223 degrees of freedom
Multiple R-squared:  0.01835,   Adjusted R-squared:  0.0007392 
F-statistic: 1.042 on 4 and 223 DF,  p-value: 0.3864

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MCP1_rank 
Effect size...............: 8.59175 
Standard error............: 14.37254 
Odds ratio (effect size)..: 5387.032 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.236373e+15 
T-value...................: 0.597789 
P-value...................: 0.5505871 
R^2.......................: 0.018347 
Adjusted r^2..............: 0.000739 
Sample size of AE DB......: 622 
Sample size of model......: 228 
Missing data %............: 63.34405 

- processing MIP1a_rank
filter: removed 408 rows (66%), 214 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -720.73294       0.06951  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-190.55 -110.64  -61.30   14.51 1520.83 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -707.87314  477.03085  -1.484   0.1393  
currentDF[, TRAIT]    8.14128   14.57152   0.559   0.5770  
Age                  -0.41962    1.77245  -0.237   0.8131  
Gendermale           11.89573   33.91094   0.351   0.7261  
ORdate_epoch          0.07003    0.03665   1.910   0.0574 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 211.9 on 209 degrees of freedom
Multiple R-squared:  0.01944,   Adjusted R-squared:  0.0006685 
F-statistic: 1.036 on 4 and 209 DF,  p-value: 0.3898

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 8.141281 
Standard error............: 14.57152 
Odds ratio (effect size)..: 3433.312 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.694508e+15 
T-value...................: 0.558712 
P-value...................: 0.5769566 
R^2.......................: 0.019435 
Adjusted r^2..............: 0.000668 
Sample size of AE DB......: 622 
Sample size of model......: 214 
Missing data %............: 65.59485 

- processing RANTES_rank
filter: removed 396 rows (64%), 226 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -724.14436       0.06987  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-196.27 -111.21  -70.34   21.32 1510.49 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.039e+03  5.242e+02  -1.982   0.0488 *
currentDF[, TRAIT]  2.049e+01  1.575e+01   1.301   0.1947  
Age                 4.738e-01  1.748e+00   0.271   0.7866  
Gendermale          8.943e+00  3.280e+01   0.273   0.7854  
ORdate_epoch        9.181e-02  3.980e-02   2.307   0.0220 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 211.1 on 221 degrees of freedom
Multiple R-squared:  0.02427,   Adjusted R-squared:  0.006613 
F-statistic: 1.374 on 4 and 221 DF,  p-value: 0.2437

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: RANTES_rank 
Effect size...............: 20.48573 
Standard error............: 15.74789 
Odds ratio (effect size)..: 788571976 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.003143e+22 
T-value...................: 1.300856 
P-value...................: 0.1946624 
R^2.......................: 0.024274 
Adjusted r^2..............: 0.006613 
Sample size of AE DB......: 622 
Sample size of model......: 226 
Missing data %............: 63.6656 

- processing MIG_rank
filter: removed 395 rows (64%), 227 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -739.66729       0.07099  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-198.05 -112.51  -60.72   12.85 1534.45 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -653.10232  485.46691  -1.345    0.180
currentDF[, TRAIT]   11.97220   14.89654   0.804    0.422
Age                   0.21629    1.73024   0.125    0.901
Gendermale            6.63324   32.60886   0.203    0.839
ORdate_epoch          0.06253    0.03787   1.651    0.100

Residual standard error: 210.6 on 222 degrees of freedom
Multiple R-squared:  0.02027,   Adjusted R-squared:  0.002617 
F-statistic: 1.148 on 4 and 222 DF,  p-value: 0.3347

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MIG_rank 
Effect size...............: 11.9722 
Standard error............: 14.89654 
Odds ratio (effect size)..: 158292 
Lower 95% CI..............: 0 
Upper 95% CI..............: 7.579701e+17 
T-value...................: 0.80369 
P-value...................: 0.422436 
R^2.......................: 0.02027 
Adjusted r^2..............: 0.002617 
Sample size of AE DB......: 622 
Sample size of model......: 227 
Missing data %............: 63.50482 

- processing IP10_rank
filter: removed 415 rows (67%), 207 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -855.34853       0.08051  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-215.74 -113.49  -58.96    9.67 1516.85 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -886.87851  496.23459  -1.787   0.0754 .
currentDF[, TRAIT]   14.14993   15.27980   0.926   0.3555  
Age                  -0.12529    1.86540  -0.067   0.9465  
Gendermale           25.15993   34.05954   0.739   0.4609  
ORdate_epoch          0.08222    0.03800   2.164   0.0317 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 215.8 on 202 degrees of freedom
Multiple R-squared:  0.02872,   Adjusted R-squared:  0.009491 
F-statistic: 1.493 on 4 and 202 DF,  p-value: 0.2055

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IP10_rank 
Effect size...............: 14.14993 
Standard error............: 15.2798 
Odds ratio (effect size)..: 1397127 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.417957e+19 
T-value...................: 0.926055 
P-value...................: 0.3555226 
R^2.......................: 0.028724 
Adjusted r^2..............: 0.009491 
Sample size of AE DB......: 622 
Sample size of model......: 207 
Missing data %............: 66.72026 

- processing Eotaxin1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -736.7299        0.0708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-172.41 -110.21  -61.95   17.21 1510.64 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -763.69961  472.27883  -1.617   0.1073  
currentDF[, TRAIT]   -2.42001   14.14447  -0.171   0.8643  
Age                  -0.01634    1.70404  -0.010   0.9924  
Gendermale           10.24147   32.28179   0.317   0.7513  
ORdate_epoch          0.07242    0.03647   1.986   0.0483 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared:  0.01748,   Adjusted R-squared:  7.917e-06 
F-statistic:     1 on 4 and 225 DF,  p-value: 0.4081

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: -2.420015 
Standard error............: 14.14447 
Odds ratio (effect size)..: 0.089 
Lower 95% CI..............: 0 
Upper 95% CI..............: 97502826964 
T-value...................: -0.171093 
P-value...................: 0.8643047 
R^2.......................: 0.017475 
Adjusted r^2..............: 8e-06 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing TARC_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      160.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-191.06 -116.19  -76.95   23.38 1528.82 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -727.73097  628.73394  -1.157    0.248
currentDF[, TRAIT]    9.51033   16.90752   0.562    0.574
Age                   0.48887    1.93885   0.252    0.801
Gendermale           14.24210   36.42162   0.391    0.696
ORdate_epoch          0.06691    0.04749   1.409    0.160

Residual standard error: 221.7 on 198 degrees of freedom
Multiple R-squared:  0.01029,   Adjusted R-squared:  -0.009703 
F-statistic: 0.5147 on 4 and 198 DF,  p-value: 0.725

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: TARC_rank 
Effect size...............: 9.510326 
Standard error............: 16.90752 
Odds ratio (effect size)..: 13498.39 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.328518e+18 
T-value...................: 0.562491 
P-value...................: 0.5744178 
R^2.......................: 0.010291 
Adjusted r^2..............: -0.009703 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing PARC_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -996.16569            23.75445             0.09146  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-192.11 -110.59  -68.69   16.91 1483.62 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -1.037e+03  4.955e+02  -2.093   0.0375 *
currentDF[, TRAIT]  2.439e+01  1.474e+01   1.654   0.0995 .
Age                 1.879e-01  1.694e+00   0.111   0.9118  
Gendermale          1.411e+01  3.206e+01   0.440   0.6603  
ORdate_epoch        9.286e-02  3.806e-02   2.440   0.0155 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 208.8 on 225 degrees of freedom
Multiple R-squared:  0.02915,   Adjusted R-squared:  0.01189 
F-statistic: 1.689 on 4 and 225 DF,  p-value: 0.1534

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: PARC_rank 
Effect size...............: 24.38967 
Standard error............: 14.7444 
Odds ratio (effect size)..: 39111065419 
Lower 95% CI..............: 0.011 
Upper 95% CI..............: 1.389897e+23 
T-value...................: 1.654166 
P-value...................: 0.09948824 
R^2.......................: 0.029154 
Adjusted r^2..............: 0.011894 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing MDC_rank
filter: removed 407 rows (65%), 215 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -709.24012       0.06856  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-195.67 -110.70  -61.38   19.68 1523.73 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -807.16415  501.83529  -1.608   0.1092  
currentDF[, TRAIT]    9.78188   15.26934   0.641   0.5225  
Age                  -0.36804    1.77425  -0.207   0.8359  
Gendermale           16.04149   33.53972   0.478   0.6329  
ORdate_epoch          0.07736    0.03845   2.012   0.0455 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 211.5 on 210 degrees of freedom
Multiple R-squared:  0.0197,    Adjusted R-squared:  0.001031 
F-statistic: 1.055 on 4 and 210 DF,  p-value: 0.3798

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MDC_rank 
Effect size...............: 9.78188 
Standard error............: 15.26934 
Odds ratio (effect size)..: 17709.92 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.760912e+17 
T-value...................: 0.640622 
P-value...................: 0.5224664 
R^2.......................: 0.019703 
Adjusted r^2..............: 0.001031 
Sample size of AE DB......: 622 
Sample size of model......: 215 
Missing data %............: 65.43408 

- processing OPG_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -736.7299        0.0708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-177.42 -110.19  -62.30   15.05 1515.23 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -751.13958  467.33991  -1.607   0.1094  
currentDF[, TRAIT]    2.96552   14.01556   0.212   0.8326  
Age                   0.04456    1.71301   0.026   0.9793  
Gendermale            9.40356   32.17821   0.292   0.7704  
ORdate_epoch          0.07115    0.03603   1.975   0.0495 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared:  0.01754,   Adjusted R-squared:  7.678e-05 
F-statistic: 1.004 on 4 and 225 DF,  p-value: 0.406

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: OPG_rank 
Effect size...............: 2.965519 
Standard error............: 14.01556 
Odds ratio (effect size)..: 19.405 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.65271e+13 
T-value...................: 0.211588 
P-value...................: 0.8326203 
R^2.......................: 0.017543 
Adjusted r^2..............: 7.7e-05 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing sICAM1_rank
filter: removed 392 rows (63%), 230 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -736.7299        0.0708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-169.49 -110.20  -62.17   17.74 1514.84 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -7.523e+02  4.823e+02  -1.560   0.1203  
currentDF[, TRAIT]  2.714e-02  1.439e+01   0.002   0.9985  
Age                 1.866e-03  1.719e+00   0.001   0.9991  
Gendermale          9.732e+00  3.218e+01   0.302   0.7626  
ORdate_epoch        7.145e-02  3.690e-02   1.936   0.0541 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210.1 on 225 degrees of freedom
Multiple R-squared:  0.01735,   Adjusted R-squared:  -0.0001222 
F-statistic: 0.993 on 4 and 225 DF,  p-value: 0.4122

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 0.027138 
Standard error............: 14.3938 
Odds ratio (effect size)..: 1.028 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.836672e+12 
T-value...................: 0.001885 
P-value...................: 0.9984973 
R^2.......................: 0.017347 
Adjusted r^2..............: -0.000122 
Sample size of AE DB......: 622 
Sample size of model......: 230 
Missing data %............: 63.02251 

- processing VEGFA_rank
filter: removed 421 rows (68%), 201 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -641.10064       0.06311  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-194.94 -109.52  -60.01   29.60  991.24 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)        -737.41480  534.11728  -1.381    0.169
currentDF[, TRAIT]   -7.42427   15.80428  -0.470    0.639
Age                   0.87765    1.75160   0.501    0.617
Gendermale          -21.18834   32.67889  -0.648    0.517
ORdate_epoch          0.06729    0.04198   1.603    0.111

Residual standard error: 200.7 on 196 degrees of freedom
Multiple R-squared:  0.01929,   Adjusted R-squared:  -0.0007252 
F-statistic: 0.9638 on 4 and 196 DF,  p-value: 0.4285

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -7.424273 
Standard error............: 15.80428 
Odds ratio (effect size)..: 0.001 
Lower 95% CI..............: 0 
Upper 95% CI..............: 16926108468 
T-value...................: -0.469763 
P-value...................: 0.6390463 
R^2.......................: 0.019289 
Adjusted r^2..............: -0.000725 
Sample size of AE DB......: 622 
Sample size of model......: 201 
Missing data %............: 67.68489 

- processing TGFB_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -911.05113            19.16758             0.08469  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-210.66 -111.76  -57.87   31.65  947.70 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -962.17837  435.55717  -2.209   0.0282 *
currentDF[, TRAIT]   19.10582   13.07276   1.461   0.1453  
Age                   1.26021    1.57578   0.800   0.4247  
Gendermale          -14.60983   29.35093  -0.498   0.6191  
ORdate_epoch          0.08283    0.03364   2.462   0.0146 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.1 on 226 degrees of freedom
Multiple R-squared:  0.0361,    Adjusted R-squared:  0.01903 
F-statistic: 2.116 on 4 and 226 DF,  p-value: 0.07973

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: TGFB_rank 
Effect size...............: 19.10582 
Standard error............: 13.07276 
Odds ratio (effect size)..: 198403954 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 2.662604e+19 
T-value...................: 1.461498 
P-value...................: 0.1452677 
R^2.......................: 0.036095 
Adjusted r^2..............: 0.019035 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP2_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -728.15695       0.07013  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-176.98 -108.67  -59.91   21.76 1525.06 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -680.26776  474.74666  -1.433   0.1533  
currentDF[, TRAIT]   -7.21999   14.11863  -0.511   0.6096  
Age                  -0.25747    1.68175  -0.153   0.8785  
Gendermale            3.67236   31.78726   0.116   0.9081  
ORdate_epoch          0.06748    0.03696   1.826   0.0692 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 208.1 on 226 degrees of freedom
Multiple R-squared:  0.01744,   Adjusted R-squared:  5.002e-05 
F-statistic: 1.003 on 4 and 226 DF,  p-value: 0.4068

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MMP2_rank 
Effect size...............: -7.219993 
Standard error............: 14.11863 
Odds ratio (effect size)..: 0.001 
Lower 95% CI..............: 0 
Upper 95% CI..............: 762805727 
T-value...................: -0.511381 
P-value...................: 0.6095838 
R^2.......................: 0.01744 
Adjusted r^2..............: 5e-05 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP8_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -728.15695       0.07013  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-200.97 -108.77  -61.77   21.18 1516.19 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -709.85753  465.65318  -1.524   0.1288  
currentDF[, TRAIT]   17.56734   13.77906   1.275   0.2036  
Age                  -0.14132    1.67030  -0.085   0.9326  
Gendermale            1.69352   31.50543   0.054   0.9572  
ORdate_epoch          0.06933    0.03641   1.904   0.0582 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 207.4 on 226 degrees of freedom
Multiple R-squared:  0.02333,   Adjusted R-squared:  0.006042 
F-statistic:  1.35 on 4 and 226 DF,  p-value: 0.2525

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MMP8_rank 
Effect size...............: 17.56734 
Standard error............: 13.77906 
Odds ratio (effect size)..: 42598844 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.282273e+19 
T-value...................: 1.27493 
P-value...................: 0.203643 
R^2.......................: 0.023328 
Adjusted r^2..............: 0.006042 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 

- processing MMP9_rank
filter: removed 391 rows (63%), 231 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -811.68182            22.99164             0.07679  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-198.31 -108.81  -63.71   18.88 1519.29 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -810.18311  467.15318  -1.734   0.0842 .
currentDF[, TRAIT]   22.99175   13.73461   1.674   0.0955 .
Age                  -0.14022    1.66589  -0.084   0.9330  
Gendermale            6.52146   31.22654   0.209   0.8348  
ORdate_epoch          0.07705    0.03653   2.109   0.0360 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 206.9 on 226 degrees of freedom
Multiple R-squared:  0.02835,   Adjusted R-squared:  0.01115 
F-statistic: 1.649 on 4 and 226 DF,  p-value: 0.163

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MMP9_rank 
Effect size...............: 22.99175 
Standard error............: 13.73461 
Odds ratio (effect size)..: 9664752433 
Lower 95% CI..............: 0.02 
Upper 95% CI..............: 4.745957e+21 
T-value...................: 1.674001 
P-value...................: 0.09551367 
R^2.......................: 0.028351 
Adjusted r^2..............: 0.011154 
Sample size of AE DB......: 622 
Sample size of model......: 231 
Missing data %............: 62.86174 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque PCSK9 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of CXCL10.

- processing IL2_rank
filter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + BMI, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch           BMI  
  -26.965065      0.002013      0.150337  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8102 -2.1998 -0.8173  0.5525 29.0543 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -26.613781  14.891061  -1.787   0.0760 .
currentDF[, TRAIT]          0.233249   0.384840   0.606   0.5454  
Age                        -0.045868   0.051403  -0.892   0.3737  
Gendermale                  0.898614   0.924728   0.972   0.3328  
ORdate_epoch                0.002064   0.001062   1.943   0.0539 .
Hypertension.compositeyes  -0.361148   1.203986  -0.300   0.7646  
DiabetesStatusDiabetes     -0.418556   1.008852  -0.415   0.6788  
SmokerStatusEx-smoker       1.109738   0.864598   1.284   0.2013  
SmokerStatusNever smoked    0.829336   1.177200   0.704   0.4822  
Med.Statin.LLDyes          -0.009352   0.849342  -0.011   0.9912  
Med.all.antiplateletyes     0.839985   1.345878   0.624   0.5335  
GFR_MDRD                   -0.024966   0.022087  -1.130   0.2602  
BMI                         0.135437   0.114595   1.182   0.2392  
MedHx_CVDNo                -0.825816   0.803556  -1.028   0.3058  
stenose70-90%               3.634166   3.438166   1.057   0.2923  
stenose90-99%               2.539691   3.416149   0.743   0.4584  
stenose100% (Occlusion)     3.495347   4.975688   0.702   0.4835  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.586 on 146 degrees of freedom
Multiple R-squared:  0.0945,    Adjusted R-squared:  -0.004732 
F-statistic: 0.9523 on 16 and 146 DF,  p-value: 0.5118

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL2_rank 
Effect size...............: 0.233249 
Standard error............: 0.38484 
Odds ratio (effect size)..: 1.263 
Lower 95% CI..............: 0.594 
Upper 95% CI..............: 2.685 
T-value...................: 0.606094 
P-value...................: 0.5453937 
R^2.......................: 0.094501 
Adjusted r^2..............: -0.004732 
Sample size of AE DB......: 622 
Sample size of model......: 163 
Missing data %............: 73.79421 

- processing IL4_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch      GFR_MDRD  
  -28.954036      0.002731     -0.037274  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.4544 -2.3573 -0.8411  0.5571 28.7159 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -30.671082  17.575951  -1.745   0.0834 .
currentDF[, TRAIT]          0.236251   0.453776   0.521   0.6035  
Age                        -0.057861   0.059475  -0.973   0.3325  
Gendermale                  1.377685   1.062388   1.297   0.1970  
ORdate_epoch                0.002784   0.001261   2.208   0.0290 *
Hypertension.compositeyes  -0.330136   1.395449  -0.237   0.8134  
DiabetesStatusDiabetes     -0.956007   1.160746  -0.824   0.4117  
SmokerStatusEx-smoker       1.036455   0.968320   1.070   0.2865  
SmokerStatusNever smoked    1.053106   1.375967   0.765   0.4455  
Med.Statin.LLDyes          -0.235247   1.015624  -0.232   0.8172  
Med.all.antiplateletyes     0.869773   1.524430   0.571   0.5693  
GFR_MDRD                   -0.048440   0.027695  -1.749   0.0827 .
BMI                         0.057516   0.138698   0.415   0.6791  
MedHx_CVDNo                -0.457341   0.924079  -0.495   0.6215  
stenose70-90%               3.620743   3.759361   0.963   0.3373  
stenose90-99%               2.017297   3.729322   0.541   0.5895  
stenose100% (Occlusion)     2.602158   5.449010   0.478   0.6338  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.953 on 128 degrees of freedom
Multiple R-squared:  0.1154,    Adjusted R-squared:  0.004871 
F-statistic: 1.044 on 16 and 128 DF,  p-value: 0.4158

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL4_rank 
Effect size...............: 0.236251 
Standard error............: 0.453776 
Odds ratio (effect size)..: 1.266 
Lower 95% CI..............: 0.52 
Upper 95% CI..............: 3.082 
T-value...................: 0.520633 
P-value...................: 0.6035218 
R^2.......................: 0.115441 
Adjusted r^2..............: 0.004871 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing IL5_rank
filter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD + 
    BMI, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch      GFR_MDRD           BMI  
  -30.628312      0.002492     -0.032294      0.156786  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1546 -2.4853 -1.0374  0.4801 28.7209 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -31.159925  16.068294  -1.939   0.0545 .
currentDF[, TRAIT]          0.255883   0.409682   0.625   0.5333  
Age                        -0.033012   0.056703  -0.582   0.5614  
Gendermale                  1.455574   0.988904   1.472   0.1433  
ORdate_epoch                0.002418   0.001172   2.063   0.0410 *
Hypertension.compositeyes  -0.481366   1.310381  -0.367   0.7139  
DiabetesStatusDiabetes     -0.742177   1.065178  -0.697   0.4871  
SmokerStatusEx-smoker       0.694868   0.900839   0.771   0.4418  
SmokerStatusNever smoked    0.868728   1.300973   0.668   0.5054  
Med.Statin.LLDyes           0.278503   0.926781   0.301   0.7642  
Med.all.antiplateletyes     0.967342   1.435599   0.674   0.5015  
GFR_MDRD                   -0.037432   0.024618  -1.521   0.1306  
BMI                         0.159403   0.122213   1.304   0.1943  
MedHx_CVDNo                -0.548693   0.874178  -0.628   0.5312  
stenose70-90%               2.907378   3.025792   0.961   0.3383  
stenose90-99%               1.689514   2.988847   0.565   0.5728  
stenose100% (Occlusion)     3.185808   4.830442   0.660   0.5106  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.84 on 141 degrees of freedom
Multiple R-squared:  0.1006,    Adjusted R-squared:  -0.001499 
F-statistic: 0.9853 on 16 and 141 DF,  p-value: 0.4761

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL5_rank 
Effect size...............: 0.255883 
Standard error............: 0.409682 
Odds ratio (effect size)..: 1.292 
Lower 95% CI..............: 0.579 
Upper 95% CI..............: 2.883 
T-value...................: 0.624589 
P-value...................: 0.5332504 
R^2.......................: 0.100565 
Adjusted r^2..............: -0.001499 
Sample size of AE DB......: 622 
Sample size of model......: 158 
Missing data %............: 74.59807 

- processing IL6_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -20.823185      0.001846  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6223 -2.3248 -0.9519  0.2217 29.0233 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -24.070206  16.225130  -1.484    0.140
currentDF[, TRAIT]          0.180702   0.388833   0.465    0.643
Age                        -0.033338   0.056834  -0.587    0.558
Gendermale                  1.152792   0.935371   1.232    0.220
ORdate_epoch                0.001741   0.001081   1.611    0.109
Hypertension.compositeyes  -0.212134   1.249760  -0.170    0.865
DiabetesStatusDiabetes     -1.051638   1.045672  -1.006    0.316
SmokerStatusEx-smoker       0.989081   0.897101   1.103    0.272
SmokerStatusNever smoked    0.305141   1.244661   0.245    0.807
Med.Statin.LLDyes           0.124275   0.884217   0.141    0.888
Med.all.antiplateletyes     0.905033   1.433893   0.631    0.529
GFR_MDRD                   -0.024336   0.024167  -1.007    0.316
BMI                         0.094031   0.103657   0.907    0.366
MedHx_CVDNo                -0.381180   0.843062  -0.452    0.652
stenose50-70%               2.243442   5.767757   0.389    0.698
stenose70-90%               4.847430   5.011080   0.967    0.335
stenose90-99%               3.928935   5.001896   0.785    0.433
stenose100% (Occlusion)     4.403998   6.314089   0.697    0.487

Residual standard error: 4.809 on 146 degrees of freedom
Multiple R-squared:  0.07929,   Adjusted R-squared:  -0.02791 
F-statistic: 0.7396 on 17 and 146 DF,  p-value: 0.758

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL6_rank 
Effect size...............: 0.180702 
Standard error............: 0.388833 
Odds ratio (effect size)..: 1.198 
Lower 95% CI..............: 0.559 
Upper 95% CI..............: 2.567 
T-value...................: 0.464729 
P-value...................: 0.6428177 
R^2.......................: 0.079292 
Adjusted r^2..............: -0.027914 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL8_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch      GFR_MDRD  
  -17.146035      0.001704     -0.028101  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5794 -2.1978 -0.9879  0.1948 28.8071 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -25.372884  16.798184  -1.510   0.1332  
currentDF[, TRAIT]          0.086622   0.405964   0.213   0.8314  
Age                        -0.070141   0.054942  -1.277   0.2039  
Gendermale                  0.624371   0.981022   0.636   0.5256  
ORdate_epoch                0.002072   0.001118   1.853   0.0660 .
Hypertension.compositeyes  -0.267889   1.216886  -0.220   0.8261  
DiabetesStatusDiabetes     -1.172524   1.030836  -1.137   0.2574  
SmokerStatusEx-smoker       0.649011   0.920980   0.705   0.4822  
SmokerStatusNever smoked   -0.214129   1.325466  -0.162   0.8719  
Med.Statin.LLDyes          -0.316973   0.881968  -0.359   0.7199  
Med.all.antiplateletyes     1.321010   1.358834   0.972   0.3327  
GFR_MDRD                   -0.038620   0.021732  -1.777   0.0778 .
BMI                         0.113641   0.103837   1.094   0.2757  
MedHx_CVDNo                -0.389126   0.859707  -0.453   0.6515  
stenose50-70%               2.958100   5.629879   0.525   0.6001  
stenose70-90%               5.005964   4.916747   1.018   0.3104  
stenose90-99%               4.920146   4.898863   1.004   0.3170  
stenose100% (Occlusion)     6.072214   6.993967   0.868   0.3868  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.7 on 136 degrees of freedom
Multiple R-squared:  0.08371,   Adjusted R-squared:  -0.03083 
F-statistic: 0.7308 on 17 and 136 DF,  p-value: 0.7669

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL8_rank 
Effect size...............: 0.086622 
Standard error............: 0.405964 
Odds ratio (effect size)..: 1.09 
Lower 95% CI..............: 0.492 
Upper 95% CI..............: 2.416 
T-value...................: 0.213373 
P-value...................: 0.8313554 
R^2.......................: 0.083707 
Adjusted r^2..............: -0.03083 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing IL9_rank
filter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Hypertension.composite, 
    data = currentDF)

Coefficients:
              (Intercept)               ORdate_epoch  Hypertension.compositeyes  
               -16.600936                   0.001387                   1.535080  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-5.122 -2.296 -1.159  0.441 29.309 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.009e+01  1.385e+01  -1.450   0.1489  
currentDF[, TRAIT]         4.150e-01  3.488e-01   1.190   0.2358  
Age                       -4.446e-02  5.076e-02  -0.876   0.3824  
Gendermale                 5.943e-01  8.415e-01   0.706   0.4810  
ORdate_epoch               1.536e-03  9.074e-04   1.693   0.0923 .
Hypertension.compositeyes  1.260e+00  1.136e+00   1.109   0.2691  
DiabetesStatusDiabetes    -1.257e+00  9.405e-01  -1.336   0.1833  
SmokerStatusEx-smoker      5.005e-01  8.221e-01   0.609   0.5435  
SmokerStatusNever smoked  -3.921e-01  1.047e+00  -0.375   0.7084  
Med.Statin.LLDyes          5.199e-01  8.313e-01   0.625   0.5325  
Med.all.antiplateletyes    7.056e-01  1.374e+00   0.513   0.6083  
GFR_MDRD                  -1.496e-02  1.912e-02  -0.782   0.4351  
BMI                        2.305e-02  9.260e-02   0.249   0.8037  
MedHx_CVDNo                1.917e-01  7.598e-01   0.252   0.8011  
stenose50-70%              1.406e+00  5.914e+00   0.238   0.8123  
stenose70-90%              4.108e+00  4.792e+00   0.857   0.3926  
stenose90-99%              3.665e+00  4.772e+00   0.768   0.4435  
stenose100% (Occlusion)    4.244e+00  6.070e+00   0.699   0.4854  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.635 on 168 degrees of freedom
Multiple R-squared:  0.07148,   Adjusted R-squared:  -0.02248 
F-statistic: 0.7608 on 17 and 168 DF,  p-value: 0.7357

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL9_rank 
Effect size...............: 0.415021 
Standard error............: 0.348802 
Odds ratio (effect size)..: 1.514 
Lower 95% CI..............: 0.764 
Upper 95% CI..............: 3 
T-value...................: 1.189849 
P-value...................: 0.2357847 
R^2.......................: 0.071479 
Adjusted r^2..............: -0.022479 
Sample size of AE DB......: 622 
Sample size of model......: 186 
Missing data %............: 70.09646 

- processing IL10_rank
filter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + SmokerStatus, 
    data = currentDF)

Coefficients:
             (Intercept)              ORdate_epoch     SmokerStatusEx-smoker  SmokerStatusNever smoked  
              -25.428594                  0.002138                  1.830375                  1.457255  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3385 -2.1954 -0.8307  0.8579 28.2056 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -32.444912  17.956566  -1.807   0.0732 .
currentDF[, TRAIT]          0.665837   0.435987   1.527   0.1293  
Age                        -0.042987   0.058584  -0.734   0.4645  
Gendermale                  1.089040   1.032505   1.055   0.2936  
ORdate_epoch                0.002616   0.001256   2.082   0.0394 *
Hypertension.compositeyes  -0.832566   1.365092  -0.610   0.5431  
DiabetesStatusDiabetes     -0.713962   1.127687  -0.633   0.5278  
SmokerStatusEx-smoker       1.376722   0.953165   1.444   0.1512  
SmokerStatusNever smoked    1.625991   1.326483   1.226   0.2226  
Med.Statin.LLDyes           0.074253   0.950585   0.078   0.9379  
Med.all.antiplateletyes     0.849033   1.477770   0.575   0.5667  
GFR_MDRD                   -0.041386   0.027093  -1.528   0.1292  
BMI                         0.136605   0.133844   1.021   0.3095  
MedHx_CVDNo                -0.983711   0.935104  -1.052   0.2949  
stenose70-90%               3.878432   3.606751   1.075   0.2844  
stenose90-99%               2.546335   3.577950   0.712   0.4780  
stenose100% (Occlusion)     4.104626   5.233282   0.784   0.4344  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.738 on 122 degrees of freedom
Multiple R-squared:  0.133, Adjusted R-squared:  0.01926 
F-statistic: 1.169 on 16 and 122 DF,  p-value: 0.302

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL10_rank 
Effect size...............: 0.665837 
Standard error............: 0.435987 
Odds ratio (effect size)..: 1.946 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 4.574 
T-value...................: 1.527194 
P-value...................: 0.1293012 
R^2.......................: 0.132971 
Adjusted r^2..............: 0.019263 
Sample size of AE DB......: 622 
Sample size of model......: 139 
Missing data %............: 77.65273 

- processing IL12_rank
filter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -29.230557      0.002538  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.2329 -2.4963 -0.8780  0.4558 28.4544 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -33.228837  18.398873  -1.806   0.0732 .
currentDF[, TRAIT]          0.410471   0.447604   0.917   0.3608  
Age                        -0.038350   0.061749  -0.621   0.5357  
Gendermale                  1.345566   1.058616   1.271   0.2060  
ORdate_epoch                0.002689   0.001281   2.099   0.0378 *
Hypertension.compositeyes  -0.784181   1.439824  -0.545   0.5869  
DiabetesStatusDiabetes     -0.833964   1.147111  -0.727   0.4685  
SmokerStatusEx-smoker       0.873933   1.018028   0.858   0.3922  
SmokerStatusNever smoked    0.821565   1.350824   0.608   0.5441  
Med.Statin.LLDyes           0.146453   1.013290   0.145   0.8853  
Med.all.antiplateletyes     1.311922   1.540053   0.852   0.3959  
GFR_MDRD                   -0.037668   0.027256  -1.382   0.1694  
BMI                         0.096007   0.137916   0.696   0.4876  
MedHx_CVDNo                -0.576829   0.929073  -0.621   0.5358  
stenose70-90%               3.816594   3.771131   1.012   0.3134  
stenose90-99%               2.610975   3.756064   0.695   0.4882  
stenose100% (Occlusion)     4.372178   6.607627   0.662   0.5094  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.98 on 129 degrees of freedom
Multiple R-squared:  0.1007,    Adjusted R-squared:  -0.01089 
F-statistic: 0.9024 on 16 and 129 DF,  p-value: 0.5679

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL12_rank 
Effect size...............: 0.410471 
Standard error............: 0.447604 
Odds ratio (effect size)..: 1.508 
Lower 95% CI..............: 0.627 
Upper 95% CI..............: 3.625 
T-value...................: 0.917041 
P-value...................: 0.3608322 
R^2.......................: 0.100656 
Adjusted r^2..............: -0.01089 
Sample size of AE DB......: 622 
Sample size of model......: 146 
Missing data %............: 76.52733 

- processing IL13_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.817355      0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5171 -2.2312 -1.1643  0.2673 29.5819 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.072e+01  1.361e+01  -1.523   0.1296  
currentDF[, TRAIT]         3.556e-01  3.505e-01   1.015   0.3116  
Age                       -2.353e-02  4.839e-02  -0.486   0.6273  
Gendermale                 5.905e-01  8.116e-01   0.728   0.4678  
ORdate_epoch               1.495e-03  9.026e-04   1.656   0.0993 .
Hypertension.compositeyes  3.562e-01  1.060e+00   0.336   0.7372  
DiabetesStatusDiabetes    -1.114e+00  8.774e-01  -1.270   0.2058  
SmokerStatusEx-smoker      5.762e-01  7.916e-01   0.728   0.4676  
SmokerStatusNever smoked  -3.711e-01  1.036e+00  -0.358   0.7206  
Med.Statin.LLDyes          1.709e-01  7.870e-01   0.217   0.8283  
Med.all.antiplateletyes    7.402e-01  1.225e+00   0.604   0.5466  
GFR_MDRD                  -9.251e-03  1.876e-02  -0.493   0.6225  
BMI                        5.320e-02  8.781e-02   0.606   0.5453  
MedHx_CVDNo               -5.676e-04  7.174e-01  -0.001   0.9994  
stenose50-70%              1.230e+00  5.561e+00   0.221   0.8253  
stenose70-90%              3.826e+00  4.815e+00   0.795   0.4278  
stenose90-99%              3.211e+00  4.809e+00   0.668   0.5051  
stenose100% (Occlusion)    3.658e+00  6.029e+00   0.607   0.5448  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.658 on 184 degrees of freedom
Multiple R-squared:  0.05661,   Adjusted R-squared:  -0.03055 
F-statistic: 0.6495 on 17 and 184 DF,  p-value: 0.8485

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL13_rank 
Effect size...............: 0.35565 
Standard error............: 0.350519 
Odds ratio (effect size)..: 1.427 
Lower 95% CI..............: 0.718 
Upper 95% CI..............: 2.837 
T-value...................: 1.014639 
P-value...................: 0.3116104 
R^2.......................: 0.056609 
Adjusted r^2..............: -0.030552 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing IL21_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.817355      0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6783 -2.3315 -1.1407  0.1965 29.5386 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.109e+01  1.364e+01  -1.546   0.1237  
currentDF[, TRAIT]         1.937e-01  3.484e-01   0.556   0.5789  
Age                       -2.941e-02  4.805e-02  -0.612   0.5413  
Gendermale                 6.190e-01  8.147e-01   0.760   0.4483  
ORdate_epoch               1.549e-03  9.027e-04   1.716   0.0879 .
Hypertension.compositeyes  2.924e-01  1.059e+00   0.276   0.7828  
DiabetesStatusDiabetes    -1.159e+00  8.778e-01  -1.320   0.1884  
SmokerStatusEx-smoker      6.529e-01  7.884e-01   0.828   0.4087  
SmokerStatusNever smoked  -2.629e-01  1.033e+00  -0.254   0.7995  
Med.Statin.LLDyes          1.914e-01  7.898e-01   0.242   0.8088  
Med.all.antiplateletyes    7.499e-01  1.229e+00   0.610   0.5424  
GFR_MDRD                  -9.941e-03  1.878e-02  -0.529   0.5973  
BMI                        5.013e-02  8.791e-02   0.570   0.5692  
MedHx_CVDNo               -2.521e-02  7.184e-01  -0.035   0.9720  
stenose50-70%              1.514e+00  5.566e+00   0.272   0.7859  
stenose70-90%              4.004e+00  4.822e+00   0.830   0.4074  
stenose90-99%              3.448e+00  4.816e+00   0.716   0.4750  
stenose100% (Occlusion)    3.588e+00  6.042e+00   0.594   0.5533  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.667 on 184 degrees of freedom
Multiple R-squared:  0.05292,   Adjusted R-squared:  -0.03458 
F-statistic: 0.6048 on 17 and 184 DF,  p-value: 0.8857

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IL21_rank 
Effect size...............: 0.193728 
Standard error............: 0.348445 
Odds ratio (effect size)..: 1.214 
Lower 95% CI..............: 0.613 
Upper 95% CI..............: 2.403 
T-value...................: 0.555978 
P-value...................: 0.5789017 
R^2.......................: 0.052921 
Adjusted r^2..............: -0.03458 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing INFG_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -24.330215      0.002138  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9977 -2.2197 -0.9322  0.6158 28.3068 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -34.240174  17.435747  -1.964   0.0516 .
currentDF[, TRAIT]          0.730254   0.451442   1.618   0.1081  
Age                        -0.040581   0.057470  -0.706   0.4813  
Gendermale                  1.557620   1.048927   1.485   0.1399  
ORdate_epoch                0.002724   0.001180   2.309   0.0224 *
Hypertension.compositeyes  -0.652113   1.389311  -0.469   0.6396  
DiabetesStatusDiabetes     -1.190103   1.055012  -1.128   0.2613  
SmokerStatusEx-smoker       0.927986   0.950216   0.977   0.3305  
SmokerStatusNever smoked    0.641096   1.291757   0.496   0.6205  
Med.Statin.LLDyes           0.217899   0.939281   0.232   0.8169  
Med.all.antiplateletyes     1.465532   1.376741   1.064   0.2890  
GFR_MDRD                   -0.033005   0.023221  -1.421   0.1575  
BMI                         0.055095   0.107280   0.514   0.6084  
MedHx_CVDNo                 0.026523   0.905600   0.029   0.9767  
stenose50-70%               1.740992   5.813769   0.299   0.7650  
stenose70-90%               4.648722   5.072055   0.917   0.3610  
stenose90-99%               3.487388   5.043908   0.691   0.4905  
stenose100% (Occlusion)     4.691270   7.168182   0.654   0.5139  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.839 on 136 degrees of freedom
Multiple R-squared:  0.1111,    Adjusted R-squared:  4.047e-05 
F-statistic:     1 on 17 and 136 DF,  p-value: 0.4619

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: INFG_rank 
Effect size...............: 0.730254 
Standard error............: 0.451442 
Odds ratio (effect size)..: 2.076 
Lower 95% CI..............: 0.857 
Upper 95% CI..............: 5.028 
T-value...................: 1.617602 
P-value...................: 0.1080654 
R^2.......................: 0.111147 
Adjusted r^2..............: 4e-05 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing TNFA_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + GFR_MDRD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch      GFR_MDRD  
  -20.810847      0.002042     -0.033496  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-5.495 -2.473 -1.039  0.399 28.581 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -26.675872  17.880816  -1.492   0.1382  
currentDF[, TRAIT]          0.330955   0.449663   0.736   0.4631  
Age                        -0.038693   0.060744  -0.637   0.5253  
Gendermale                  1.429970   1.061071   1.348   0.1801  
ORdate_epoch                0.002138   0.001246   1.716   0.0886 .
Hypertension.compositeyes  -0.689387   1.422087  -0.485   0.6287  
DiabetesStatusDiabetes     -1.030423   1.148280  -0.897   0.3712  
SmokerStatusEx-smoker       1.025142   0.989971   1.036   0.3024  
SmokerStatusNever smoked    0.769191   1.371219   0.561   0.5758  
Med.Statin.LLDyes           0.066400   1.019920   0.065   0.9482  
Med.all.antiplateletyes     1.331928   1.530744   0.870   0.3859  
GFR_MDRD                   -0.037587   0.026376  -1.425   0.1566  
BMI                         0.107176   0.130626   0.820   0.4135  
MedHx_CVDNo                -0.296667   0.960024  -0.309   0.7578  
stenose70-90%               3.225127   3.792502   0.850   0.3967  
stenose90-99%               2.432635   3.753984   0.648   0.5181  
stenose100% (Occlusion)     4.204330   6.614458   0.636   0.5262  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 5.014 on 128 degrees of freedom
Multiple R-squared:  0.09119,   Adjusted R-squared:  -0.02241 
F-statistic: 0.8027 on 16 and 128 DF,  p-value: 0.6804

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: TNFA_rank 
Effect size...............: 0.330955 
Standard error............: 0.449663 
Odds ratio (effect size)..: 1.392 
Lower 95% CI..............: 0.577 
Upper 95% CI..............: 3.361 
T-value...................: 0.736006 
P-value...................: 0.4630739 
R^2.......................: 0.09119 
Adjusted r^2..............: -0.022412 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing MIF_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.817355      0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-4.796 -2.374 -1.067  0.196 29.685 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -20.503691  14.763319  -1.389    0.167
currentDF[, TRAIT]         -0.078909   0.404168  -0.195    0.845
Age                        -0.034423   0.047640  -0.723    0.471
Gendermale                  0.678848   0.810849   0.837    0.404
ORdate_epoch                0.001502   0.001010   1.486    0.139
Hypertension.compositeyes   0.240569   1.066024   0.226    0.822
DiabetesStatusDiabetes     -1.206368   0.877977  -1.374    0.171
SmokerStatusEx-smoker       0.691972   0.785817   0.881    0.380
SmokerStatusNever smoked   -0.141410   1.022738  -0.138    0.890
Med.Statin.LLDyes           0.241017   0.787612   0.306    0.760
Med.all.antiplateletyes     0.803801   1.232477   0.652    0.515
GFR_MDRD                   -0.010619   0.019073  -0.557    0.578
BMI                         0.049580   0.087991   0.563    0.574
MedHx_CVDNo                -0.053994   0.719365  -0.075    0.940
stenose50-70%               1.848423   5.565207   0.332    0.740
stenose70-90%               4.260740   4.833270   0.882    0.379
stenose90-99%               3.800655   4.823301   0.788    0.432
stenose100% (Occlusion)     3.705747   6.052527   0.612    0.541

Residual standard error: 4.67 on 184 degrees of freedom
Multiple R-squared:  0.05153,   Adjusted R-squared:  -0.0361 
F-statistic: 0.588 on 17 and 184 DF,  p-value: 0.8983

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MIF_rank 
Effect size...............: -0.078909 
Standard error............: 0.404168 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.418 
Upper 95% CI..............: 2.041 
T-value...................: -0.195239 
P-value...................: 0.8454212 
R^2.......................: 0.051527 
Adjusted r^2..............: -0.036104 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MCP1_rank
filter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.463601      0.001407  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0703 -2.3818 -1.1334  0.1454 29.5535 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -2.038e+01  1.409e+01  -1.447    0.150
currentDF[, TRAIT]        -1.439e-01  3.467e-01  -0.415    0.679
Age                       -3.483e-02  4.840e-02  -0.720    0.473
Gendermale                 7.015e-01  8.254e-01   0.850    0.397
ORdate_epoch               1.504e-03  9.321e-04   1.613    0.108
Hypertension.compositeyes  2.773e-01  1.073e+00   0.258    0.796
DiabetesStatusDiabetes    -1.248e+00  8.855e-01  -1.409    0.160
SmokerStatusEx-smoker      7.275e-01  7.902e-01   0.921    0.358
SmokerStatusNever smoked  -1.203e-01  1.035e+00  -0.116    0.908
Med.Statin.LLDyes          2.017e-01  7.955e-01   0.254    0.800
Med.all.antiplateletyes    6.806e-01  1.311e+00   0.519    0.604
GFR_MDRD                  -9.819e-03  1.898e-02  -0.517    0.606
BMI                        4.814e-02  8.984e-02   0.536    0.593
MedHx_CVDNo               -6.055e-02  7.267e-01  -0.083    0.934
stenose50-70%              1.830e+00  5.579e+00   0.328    0.743
stenose70-90%              4.234e+00  4.840e+00   0.875    0.383
stenose90-99%              3.752e+00  4.823e+00   0.778    0.438
stenose100% (Occlusion)    3.476e+00  6.094e+00   0.570    0.569

Residual standard error: 4.692 on 182 degrees of freedom
Multiple R-squared:  0.05082,   Adjusted R-squared:  -0.03784 
F-statistic: 0.5732 on 17 and 182 DF,  p-value: 0.9086

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MCP1_rank 
Effect size...............: -0.143879 
Standard error............: 0.346731 
Odds ratio (effect size)..: 0.866 
Lower 95% CI..............: 0.439 
Upper 95% CI..............: 1.709 
T-value...................: -0.414958 
P-value...................: 0.6786616 
R^2.......................: 0.050816 
Adjusted r^2..............: -0.037844 
Sample size of AE DB......: 622 
Sample size of model......: 200 
Missing data %............: 67.84566 

- processing MIP1a_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.387348      0.001324  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1664 -2.4323 -1.0745  0.1447 29.3884 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -2.002e+01  1.421e+01  -1.409    0.161
currentDF[, TRAIT]         8.331e-02  3.648e-01   0.228    0.820
Age                       -3.893e-02  5.057e-02  -0.770    0.442
Gendermale                 7.851e-01  8.737e-01   0.899    0.370
ORdate_epoch               1.470e-03  9.377e-04   1.567    0.119
Hypertension.compositeyes  4.160e-01  1.145e+00   0.363    0.717
DiabetesStatusDiabetes    -1.459e+00  9.436e-01  -1.546    0.124
SmokerStatusEx-smoker      7.577e-01  8.456e-01   0.896    0.371
SmokerStatusNever smoked  -2.148e-01  1.086e+00  -0.198    0.844
Med.Statin.LLDyes          2.863e-01  8.440e-01   0.339    0.735
Med.all.antiplateletyes    9.359e-01  1.417e+00   0.661    0.510
GFR_MDRD                  -1.263e-02  1.973e-02  -0.640    0.523
BMI                        5.272e-02  9.342e-02   0.564    0.573
MedHx_CVDNo                3.679e-02  7.834e-01   0.047    0.963
stenose50-70%              1.832e+00  6.144e+00   0.298    0.766
stenose70-90%              4.302e+00  4.975e+00   0.865    0.388
stenose90-99%              3.627e+00  4.967e+00   0.730    0.466
stenose100% (Occlusion)    3.789e+00  6.265e+00   0.605    0.546

Residual standard error: 4.797 on 171 degrees of freedom
Multiple R-squared:  0.05779,   Adjusted R-squared:  -0.03588 
F-statistic: 0.617 on 17 and 171 DF,  p-value: 0.8756

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 0.083311 
Standard error............: 0.364823 
Odds ratio (effect size)..: 1.087 
Lower 95% CI..............: 0.532 
Upper 95% CI..............: 2.222 
T-value...................: 0.22836 
P-value...................: 0.8196395 
R^2.......................: 0.057793 
Adjusted r^2..............: -0.035877 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing RANTES_rank
filter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.420322      0.001404  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0265 -2.3826 -1.0165  0.1318 29.6057 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.423e+01  1.466e+01  -1.652   0.1002  
currentDF[, TRAIT]         1.693e-01  3.847e-01   0.440   0.6604  
Age                       -2.847e-02  4.897e-02  -0.581   0.5617  
Gendermale                 7.307e-01  8.378e-01   0.872   0.3842  
ORdate_epoch               1.744e-03  9.886e-04   1.764   0.0794 .
Hypertension.compositeyes  3.848e-01  1.111e+00   0.346   0.7295  
DiabetesStatusDiabetes    -1.141e+00  9.058e-01  -1.259   0.2096  
SmokerStatusEx-smoker      6.926e-01  8.082e-01   0.857   0.3926  
SmokerStatusNever smoked  -2.362e-01  1.045e+00  -0.226   0.8215  
Med.Statin.LLDyes          2.816e-01  8.018e-01   0.351   0.7258  
Med.all.antiplateletyes    9.296e-01  1.308e+00   0.711   0.4782  
GFR_MDRD                  -9.678e-03  1.911e-02  -0.506   0.6132  
BMI                        5.102e-02  9.028e-02   0.565   0.5727  
MedHx_CVDNo                2.006e-02  7.497e-01   0.027   0.9787  
stenose50-70%              1.881e+00  5.982e+00   0.314   0.7535  
stenose70-90%              4.145e+00  4.880e+00   0.849   0.3967  
stenose90-99%              3.606e+00  4.855e+00   0.743   0.4586  
stenose100% (Occlusion)    3.724e+00  6.154e+00   0.605   0.5458  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.711 on 180 degrees of freedom
Multiple R-squared:  0.05263,   Adjusted R-squared:  -0.03685 
F-statistic: 0.5882 on 17 and 180 DF,  p-value: 0.898

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: RANTES_rank 
Effect size...............: 0.16933 
Standard error............: 0.384733 
Odds ratio (effect size)..: 1.185 
Lower 95% CI..............: 0.557 
Upper 95% CI..............: 2.518 
T-value...................: 0.440123 
P-value...................: 0.660376 
R^2.......................: 0.052628 
Adjusted r^2..............: -0.036846 
Sample size of AE DB......: 622 
Sample size of model......: 198 
Missing data %............: 68.1672 

- processing MIG_rank
filter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.827596      0.001435  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6510 -2.3429 -1.1303  0.1877 29.2779 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.866e+01  1.422e+01  -1.313    0.191
currentDF[, TRAIT]         3.685e-01  3.824e-01   0.964    0.337
Age                       -2.516e-02  4.874e-02  -0.516    0.606
Gendermale                 6.249e-01  8.273e-01   0.755    0.451
ORdate_epoch               1.330e-03  9.562e-04   1.391    0.166
Hypertension.compositeyes  3.531e-01  1.097e+00   0.322    0.748
DiabetesStatusDiabetes    -1.190e+00  8.972e-01  -1.326    0.186
SmokerStatusEx-smoker      5.827e-01  8.026e-01   0.726    0.469
SmokerStatusNever smoked  -3.238e-01  1.050e+00  -0.308    0.758
Med.Statin.LLDyes          2.407e-01  8.027e-01   0.300    0.765
Med.all.antiplateletyes    8.730e-01  1.262e+00   0.692    0.490
GFR_MDRD                  -1.202e-02  1.896e-02  -0.634    0.527
BMI                        5.085e-02  8.853e-02   0.574    0.566
MedHx_CVDNo               -8.141e-02  7.348e-01  -0.111    0.912
stenose50-70%              1.550e+00  5.993e+00   0.259    0.796
stenose70-90%              4.040e+00  4.847e+00   0.834    0.406
stenose90-99%              3.395e+00  4.836e+00   0.702    0.484
stenose100% (Occlusion)    3.784e+00  6.090e+00   0.621    0.535

Residual standard error: 4.689 on 181 degrees of freedom
Multiple R-squared:  0.0577,    Adjusted R-squared:  -0.0308 
F-statistic: 0.652 on 17 and 181 DF,  p-value: 0.8461

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MIG_rank 
Effect size...............: 0.368488 
Standard error............: 0.382391 
Odds ratio (effect size)..: 1.446 
Lower 95% CI..............: 0.683 
Upper 95% CI..............: 3.059 
T-value...................: 0.963642 
P-value...................: 0.3365111 
R^2.......................: 0.057702 
Adjusted r^2..............: -0.030802 
Sample size of AE DB......: 622 
Sample size of model......: 199 
Missing data %............: 68.00643 

- processing IP10_rank
filter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -15.749874            0.699569            0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-4.769 -2.434 -1.010  0.481 29.131 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.305e+01  1.440e+01  -1.600   0.1114  
currentDF[, TRAIT]         6.524e-01  3.856e-01   1.692   0.0926 .
Age                       -2.311e-02  5.263e-02  -0.439   0.6612  
Gendermale                 9.332e-01  8.639e-01   1.080   0.2816  
ORdate_epoch               1.656e-03  9.475e-04   1.747   0.0824 .
Hypertension.compositeyes  4.573e-01  1.170e+00   0.391   0.6963  
DiabetesStatusDiabetes    -1.386e+00  9.799e-01  -1.415   0.1591  
SmokerStatusEx-smoker      4.306e-01  8.720e-01   0.494   0.6221  
SmokerStatusNever smoked  -4.518e-01  1.123e+00  -0.402   0.6880  
Med.Statin.LLDyes          2.460e-01  8.647e-01   0.285   0.7764  
Med.all.antiplateletyes    9.678e-01  1.339e+00   0.723   0.4709  
GFR_MDRD                  -1.531e-02  2.056e-02  -0.745   0.4575  
BMI                        5.775e-02  9.312e-02   0.620   0.5360  
MedHx_CVDNo               -1.455e-01  7.915e-01  -0.184   0.8544  
stenose50-70%              1.242e+00  6.158e+00   0.202   0.8404  
stenose70-90%              4.168e+00  4.997e+00   0.834   0.4054  
stenose90-99%              3.421e+00  4.968e+00   0.689   0.4921  
stenose100% (Occlusion)    4.688e+00  6.276e+00   0.747   0.4561  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.815 on 165 degrees of freedom
Multiple R-squared:  0.0796,    Adjusted R-squared:  -0.01523 
F-statistic: 0.8394 on 17 and 165 DF,  p-value: 0.6459

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: IP10_rank 
Effect size...............: 0.652391 
Standard error............: 0.385601 
Odds ratio (effect size)..: 1.92 
Lower 95% CI..............: 0.902 
Upper 95% CI..............: 4.088 
T-value...................: 1.691883 
P-value...................: 0.09255598 
R^2.......................: 0.079598 
Adjusted r^2..............: -0.015231 
Sample size of AE DB......: 622 
Sample size of model......: 183 
Missing data %............: 70.57878 

- processing Eotaxin1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.817355      0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8560 -2.3946 -1.0691  0.1372 29.5904 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -21.672492  13.798457  -1.571   0.1180  
currentDF[, TRAIT]         -0.008485   0.353855  -0.024   0.9809  
Age                        -0.033809   0.047934  -0.705   0.4815  
Gendermale                  0.673504   0.816448   0.825   0.4105  
ORdate_epoch                0.001595   0.000917   1.740   0.0836 .
Hypertension.compositeyes   0.264865   1.058910   0.250   0.8028  
DiabetesStatusDiabetes     -1.196713   0.878665  -1.362   0.1749  
SmokerStatusEx-smoker       0.704952   0.787963   0.895   0.3721  
SmokerStatusNever smoked   -0.156677   1.033423  -0.152   0.8797  
Med.Statin.LLDyes           0.234698   0.788180   0.298   0.7662  
Med.all.antiplateletyes     0.785151   1.230307   0.638   0.5242  
GFR_MDRD                   -0.009999   0.018803  -0.532   0.5955  
BMI                         0.048997   0.088029   0.557   0.5785  
MedHx_CVDNo                -0.046433   0.719449  -0.065   0.9486  
stenose50-70%               1.779037   5.563732   0.320   0.7495  
stenose70-90%               4.186436   4.826052   0.867   0.3868  
stenose90-99%               3.714786   4.820948   0.771   0.4420  
stenose100% (Occlusion)     3.655400   6.052589   0.604   0.5466  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.671 on 184 degrees of freedom
Multiple R-squared:  0.05133,   Adjusted R-squared:  -0.03632 
F-statistic: 0.5857 on 17 and 184 DF,  p-value: 0.8999

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: -0.008485 
Standard error............: 0.353855 
Odds ratio (effect size)..: 0.992 
Lower 95% CI..............: 0.496 
Upper 95% CI..............: 1.984 
T-value...................: -0.023978 
P-value...................: 0.980896 
R^2.......................: 0.051333 
Adjusted r^2..............: -0.036315 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing TARC_rank
filter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      2.354  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0649 -2.4433 -1.2136  0.1619 29.3384 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -21.660845  17.433760  -1.242    0.216
currentDF[, TRAIT]          0.154566   0.417458   0.370    0.712
Age                        -0.033960   0.055210  -0.615    0.539
Gendermale                  0.783335   0.930657   0.842    0.401
ORdate_epoch                0.001505   0.001209   1.245    0.215
Hypertension.compositeyes   0.417704   1.173070   0.356    0.722
DiabetesStatusDiabetes     -1.275708   0.982040  -1.299    0.196
SmokerStatusEx-smoker       0.787567   0.890952   0.884    0.378
SmokerStatusNever smoked   -0.372759   1.138409  -0.327    0.744
Med.Statin.LLDyes           0.397465   0.926540   0.429    0.669
Med.all.antiplateletyes     1.111156   1.435978   0.774    0.440
GFR_MDRD                   -0.008284   0.021502  -0.385    0.701
BMI                         0.061739   0.099292   0.622    0.535
MedHx_CVDNo                -0.032153   0.817727  -0.039    0.969
stenose50-70%               1.726215   6.278650   0.275    0.784
stenose70-90%               4.404426   5.130168   0.859    0.392
stenose90-99%               3.761378   5.113005   0.736    0.463
stenose100% (Occlusion)     4.118905   6.510879   0.633    0.528

Residual standard error: 4.959 on 160 degrees of freedom
Multiple R-squared:  0.05309,   Adjusted R-squared:  -0.04752 
F-statistic: 0.5277 on 17 and 160 DF,  p-value: 0.936

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: TARC_rank 
Effect size...............: 0.154566 
Standard error............: 0.417458 
Odds ratio (effect size)..: 1.167 
Lower 95% CI..............: 0.515 
Upper 95% CI..............: 2.645 
T-value...................: 0.370255 
P-value...................: 0.7116825 
R^2.......................: 0.053093 
Adjusted r^2..............: -0.047516 
Sample size of AE DB......: 622 
Sample size of model......: 178 
Missing data %............: 71.38264 

- processing PARC_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -22.880077            0.598895            0.001995  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9525 -2.1971 -1.1485  0.3443 29.6106 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -3.145e+01  1.460e+01  -2.154   0.0325 *
currentDF[, TRAIT]         6.577e-01  3.714e-01   1.771   0.0782 .
Age                       -2.609e-02  4.727e-02  -0.552   0.5817  
Gendermale                 7.179e-01  8.036e-01   0.893   0.3728  
ORdate_epoch               2.258e-03  9.687e-04   2.331   0.0209 *
Hypertension.compositeyes  5.558e-01  1.063e+00   0.523   0.6016  
DiabetesStatusDiabetes    -1.097e+00  8.706e-01  -1.260   0.2091  
SmokerStatusEx-smoker      6.481e-01  7.779e-01   0.833   0.4058  
SmokerStatusNever smoked  -4.426e-01  1.022e+00  -0.433   0.6654  
Med.Statin.LLDyes          1.716e-01  7.809e-01   0.220   0.8263  
Med.all.antiplateletyes    9.142e-01  1.220e+00   0.749   0.4546  
GFR_MDRD                  -5.706e-03  1.880e-02  -0.304   0.7618  
BMI                        6.517e-02  8.769e-02   0.743   0.4584  
MedHx_CVDNo               -9.021e-02  7.125e-01  -0.127   0.8994  
stenose50-70%              1.764e+00  5.505e+00   0.321   0.7490  
stenose70-90%              4.016e+00  4.776e+00   0.841   0.4014  
stenose90-99%              3.628e+00  4.758e+00   0.763   0.4467  
stenose100% (Occlusion)    4.473e+00  6.013e+00   0.744   0.4579  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.631 on 184 degrees of freedom
Multiple R-squared:  0.06723,   Adjusted R-squared:  -0.01895 
F-statistic: 0.7801 on 17 and 184 DF,  p-value: 0.7145

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: PARC_rank 
Effect size...............: 0.657728 
Standard error............: 0.371371 
Odds ratio (effect size)..: 1.93 
Lower 95% CI..............: 0.932 
Upper 95% CI..............: 3.997 
T-value...................: 1.77108 
P-value...................: 0.07820286 
R^2.......................: 0.067232 
Adjusted r^2..............: -0.018948 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MDC_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -14.625529      0.001345  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.2135 -2.4255 -1.0951  0.1266 29.4807 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.924e+01  1.467e+01  -1.312    0.191
currentDF[, TRAIT]        -1.970e-01  3.856e-01  -0.511    0.610
Age                       -3.818e-02  5.054e-02  -0.755    0.451
Gendermale                 8.593e-01  8.701e-01   0.988    0.325
ORdate_epoch               1.364e-03  9.793e-04   1.393    0.165
Hypertension.compositeyes  4.523e-01  1.147e+00   0.394    0.694
DiabetesStatusDiabetes    -1.397e+00  9.538e-01  -1.465    0.145
SmokerStatusEx-smoker      6.918e-01  8.433e-01   0.820    0.413
SmokerStatusNever smoked  -1.709e-01  1.086e+00  -0.157    0.875
Med.Statin.LLDyes          4.104e-01  8.556e-01   0.480    0.632
Med.all.antiplateletyes    9.305e-01  1.424e+00   0.654    0.514
GFR_MDRD                  -1.104e-02  1.970e-02  -0.560    0.576
BMI                        4.997e-02  9.410e-02   0.531    0.596
MedHx_CVDNo               -1.790e-02  7.852e-01  -0.023    0.982
stenose50-70%              2.368e+00  6.163e+00   0.384    0.701
stenose70-90%              4.586e+00  4.979e+00   0.921    0.358
stenose90-99%              4.018e+00  4.962e+00   0.810    0.419
stenose100% (Occlusion)    3.977e+00  6.275e+00   0.634    0.527

Residual standard error: 4.809 on 171 degrees of freedom
Multiple R-squared:  0.056, Adjusted R-squared:  -0.03785 
F-statistic: 0.5967 on 17 and 171 DF,  p-value: 0.8914

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MDC_rank 
Effect size...............: -0.197019 
Standard error............: 0.38561 
Odds ratio (effect size)..: 0.821 
Lower 95% CI..............: 0.386 
Upper 95% CI..............: 1.749 
T-value...................: -0.510929 
P-value...................: 0.6100599 
R^2.......................: 0.055998 
Adjusted r^2..............: -0.037851 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing OPG_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.817355      0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8452 -2.3654 -1.0829  0.1547 29.6344 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.182e+01  1.364e+01  -1.600    0.111  
currentDF[, TRAIT]         8.041e-02  3.411e-01   0.236    0.814  
Age                       -3.173e-02  4.816e-02  -0.659    0.511  
Gendermale                 6.592e-01  8.114e-01   0.812    0.418  
ORdate_epoch               1.595e-03  9.002e-04   1.772    0.078 .
Hypertension.compositeyes  2.861e-01  1.063e+00   0.269    0.788  
DiabetesStatusDiabetes    -1.190e+00  8.763e-01  -1.358    0.176  
SmokerStatusEx-smoker      6.768e-01  7.916e-01   0.855    0.394  
SmokerStatusNever smoked  -1.942e-01  1.027e+00  -0.189    0.850  
Med.Statin.LLDyes          2.242e-01  7.876e-01   0.285    0.776  
Med.all.antiplateletyes    7.564e-01  1.233e+00   0.613    0.540  
GFR_MDRD                  -9.987e-03  1.880e-02  -0.531    0.596  
BMI                        5.090e-02  8.829e-02   0.576    0.565  
MedHx_CVDNo               -4.633e-02  7.180e-01  -0.065    0.949  
stenose50-70%              1.737e+00  5.552e+00   0.313    0.755  
stenose70-90%              4.194e+00  4.815e+00   0.871    0.385  
stenose90-99%              3.704e+00  4.797e+00   0.772    0.441  
stenose100% (Occlusion)    3.761e+00  6.064e+00   0.620    0.536  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.67 on 184 degrees of freedom
Multiple R-squared:  0.05162,   Adjusted R-squared:  -0.03601 
F-statistic: 0.5891 on 17 and 184 DF,  p-value: 0.8975

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: OPG_rank 
Effect size...............: 0.080414 
Standard error............: 0.341108 
Odds ratio (effect size)..: 1.084 
Lower 95% CI..............: 0.555 
Upper 95% CI..............: 2.115 
T-value...................: 0.235744 
P-value...................: 0.8138933 
R^2.......................: 0.051617 
Adjusted r^2..............: -0.036006 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing sICAM1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -15.817355      0.001434  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9499 -2.3650 -1.0368  0.2082 29.5606 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.828e+01  1.394e+01  -1.311    0.191
currentDF[, TRAIT]        -3.760e-01  3.576e-01  -1.051    0.294
Age                       -4.379e-02  4.831e-02  -0.906    0.366
Gendermale                 7.009e-01  8.080e-01   0.867    0.387
ORdate_epoch               1.351e-03  9.262e-04   1.458    0.146
Hypertension.compositeyes  1.477e-01  1.062e+00   0.139    0.890
DiabetesStatusDiabetes    -1.264e+00  8.760e-01  -1.443    0.151
SmokerStatusEx-smoker      7.360e-01  7.821e-01   0.941    0.348
SmokerStatusNever smoked   2.786e-02  1.031e+00   0.027    0.978
Med.Statin.LLDyes          2.494e-01  7.846e-01   0.318    0.751
Med.all.antiplateletyes    8.290e-01  1.225e+00   0.677    0.500
GFR_MDRD                  -1.191e-02  1.883e-02  -0.632    0.528
BMI                        5.177e-02  8.774e-02   0.590    0.556
MedHx_CVDNo               -1.313e-01  7.206e-01  -0.182    0.856
stenose50-70%              2.032e+00  5.540e+00   0.367    0.714
stenose70-90%              4.659e+00  4.823e+00   0.966    0.335
stenose90-99%              4.161e+00  4.803e+00   0.866    0.388
stenose100% (Occlusion)    4.011e+00  6.038e+00   0.664    0.507

Residual standard error: 4.657 on 184 degrees of freedom
Multiple R-squared:  0.057, Adjusted R-squared:  -0.03013 
F-statistic: 0.6542 on 17 and 184 DF,  p-value: 0.8442

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -0.375967 
Standard error............: 0.357569 
Odds ratio (effect size)..: 0.687 
Lower 95% CI..............: 0.341 
Upper 95% CI..............: 1.384 
T-value...................: -1.051453 
P-value...................: 0.2944293 
R^2.......................: 0.056996 
Adjusted r^2..............: -0.030129 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing VEGFA_rank
filter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -16.593238      0.001508  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3908 -2.4138 -1.0523  0.2214 29.2614 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -24.500832  15.330316  -1.598   0.1120  
currentDF[, TRAIT]         -0.241973   0.410471  -0.590   0.5564  
Age                        -0.032729   0.053722  -0.609   0.5432  
Gendermale                  0.635058   0.910832   0.697   0.4867  
ORdate_epoch                0.002003   0.001139   1.757   0.0808 .
Hypertension.compositeyes   0.326664   1.216976   0.268   0.7887  
DiabetesStatusDiabetes     -1.156998   0.977194  -1.184   0.2382  
SmokerStatusEx-smoker       0.953761   0.868794   1.098   0.2739  
SmokerStatusNever smoked   -0.658051   1.189873  -0.553   0.5810  
Med.Statin.LLDyes           0.515954   0.873353   0.591   0.5555  
Med.all.antiplateletyes     0.828104   1.304472   0.635   0.5265  
GFR_MDRD                   -0.007824   0.019310  -0.405   0.6859  
BMI                         0.028931   0.099908   0.290   0.7725  
MedHx_CVDNo                -0.079548   0.811143  -0.098   0.9220  
stenose70-90%               2.097588   3.741795   0.561   0.5759  
stenose90-99%               1.641623   3.706914   0.443   0.6585  
stenose100% (Occlusion)     1.235058   5.277213   0.234   0.8153  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.878 on 160 degrees of freedom
Multiple R-squared:  0.05871,   Adjusted R-squared:  -0.03542 
F-statistic: 0.6237 on 16 and 160 DF,  p-value: 0.8615

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -0.241973 
Standard error............: 0.410471 
Odds ratio (effect size)..: 0.785 
Lower 95% CI..............: 0.351 
Upper 95% CI..............: 1.755 
T-value...................: -0.589501 
P-value...................: 0.5563569 
R^2.......................: 0.058708 
Adjusted r^2..............: -0.035421 
Sample size of AE DB......: 622 
Sample size of model......: 177 
Missing data %............: 71.54341 

- processing TGFB_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -16.796255      0.001515  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1150 -2.3911 -1.1547  0.1368 29.4310 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -21.485693  13.521963  -1.589    0.114  
currentDF[, TRAIT]         -0.137804   0.343232  -0.401    0.689  
Age                        -0.033929   0.047945  -0.708    0.480  
Gendermale                  0.550802   0.804445   0.685    0.494  
ORdate_epoch                0.001597   0.000898   1.778    0.077 .
Hypertension.compositeyes   0.370379   1.045805   0.354    0.724  
DiabetesStatusDiabetes     -1.190252   0.852732  -1.396    0.164  
SmokerStatusEx-smoker       0.758312   0.783326   0.968    0.334  
SmokerStatusNever smoked   -0.208317   1.039043  -0.200    0.841  
Med.Statin.LLDyes           0.326951   0.786036   0.416    0.678  
Med.all.antiplateletyes     0.687590   1.263443   0.544    0.587  
GFR_MDRD                   -0.011180   0.018746  -0.596    0.552  
BMI                         0.040856   0.088697   0.461    0.646  
MedHx_CVDNo                -0.071165   0.717439  -0.099    0.921  
stenose50-70%               2.132694   5.585255   0.382    0.703  
stenose70-90%               4.366276   4.823873   0.905    0.367  
stenose90-99%               3.894393   4.807602   0.810    0.419  
stenose100% (Occlusion)     3.918812   6.098553   0.643    0.521  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.667 on 185 degrees of freedom
Multiple R-squared:  0.05474,   Adjusted R-squared:  -0.03213 
F-statistic: 0.6302 on 17 and 185 DF,  p-value: 0.8652

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: TGFB_rank 
Effect size...............: -0.137804 
Standard error............: 0.343232 
Odds ratio (effect size)..: 0.871 
Lower 95% CI..............: 0.445 
Upper 95% CI..............: 1.707 
T-value...................: -0.40149 
P-value...................: 0.6885225 
R^2.......................: 0.054736 
Adjusted r^2..............: -0.032126 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing MMP2_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -17.277671      0.001552  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0198 -2.2372 -1.0349  0.1006 29.1133 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.880e+01  1.378e+01  -1.365    0.174
currentDF[, TRAIT]        -4.068e-01  3.517e-01  -1.157    0.249
Age                       -4.263e-02  4.690e-02  -0.909    0.365
Gendermale                 4.714e-01  7.933e-01   0.594    0.553
ORdate_epoch               1.466e-03  9.095e-04   1.612    0.109
Hypertension.compositeyes  1.569e-01  1.073e+00   0.146    0.884
DiabetesStatusDiabetes    -8.140e-01  8.590e-01  -0.948    0.345
SmokerStatusEx-smoker      9.546e-01  7.771e-01   1.228    0.221
SmokerStatusNever smoked   1.283e-01  1.026e+00   0.125    0.901
Med.Statin.LLDyes          1.201e-01  7.717e-01   0.156    0.877
Med.all.antiplateletyes    9.118e-01  1.212e+00   0.753    0.453
GFR_MDRD                  -1.254e-02  1.798e-02  -0.698    0.486
BMI                        3.336e-02  8.940e-02   0.373    0.709
MedHx_CVDNo               -2.227e-01  7.117e-01  -0.313    0.755
stenose50-70%              2.042e+00  5.486e+00   0.372    0.710
stenose70-90%              4.324e+00  4.762e+00   0.908    0.365
stenose90-99%              3.694e+00  4.742e+00   0.779    0.437
stenose100% (Occlusion)    3.586e+00  5.983e+00   0.599    0.550

Residual standard error: 4.61 on 184 degrees of freedom
Multiple R-squared:  0.06124,   Adjusted R-squared:  -0.02549 
F-statistic: 0.7061 on 17 and 184 DF,  p-value: 0.7943

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MMP2_rank 
Effect size...............: -0.406818 
Standard error............: 0.351695 
Odds ratio (effect size)..: 0.666 
Lower 95% CI..............: 0.334 
Upper 95% CI..............: 1.326 
T-value...................: -1.156733 
P-value...................: 0.248881 
R^2.......................: 0.06124 
Adjusted r^2..............: -0.025494 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP8_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -17.277671      0.001552  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0676 -2.3426 -1.0626  0.1025 29.2867 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.141e+01  1.360e+01  -1.574   0.1172  
currentDF[, TRAIT]         1.506e-01  3.408e-01   0.442   0.6591  
Age                       -3.581e-02  4.690e-02  -0.764   0.4461  
Gendermale                 5.280e-01  8.013e-01   0.659   0.5108  
ORdate_epoch               1.640e-03  8.979e-04   1.827   0.0694 .
Hypertension.compositeyes  3.536e-01  1.062e+00   0.333   0.7396  
DiabetesStatusDiabetes    -7.837e-01  8.682e-01  -0.903   0.3679  
SmokerStatusEx-smoker      9.059e-01  7.785e-01   1.164   0.2461  
SmokerStatusNever smoked  -2.766e-02  1.020e+00  -0.027   0.9784  
Med.Statin.LLDyes          1.317e-01  7.741e-01   0.170   0.8651  
Med.all.antiplateletyes    8.484e-01  1.214e+00   0.699   0.4857  
GFR_MDRD                  -8.606e-03  1.810e-02  -0.476   0.6350  
BMI                        3.743e-02  8.977e-02   0.417   0.6772  
MedHx_CVDNo               -1.682e-01  7.172e-01  -0.234   0.8149  
stenose50-70%              1.394e+00  5.543e+00   0.251   0.8017  
stenose70-90%              3.694e+00  4.888e+00   0.756   0.4508  
stenose90-99%              3.154e+00  4.848e+00   0.651   0.5161  
stenose100% (Occlusion)    3.102e+00  6.108e+00   0.508   0.6122  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.625 on 184 degrees of freedom
Multiple R-squared:  0.05542,   Adjusted R-squared:  -0.03186 
F-statistic: 0.635 on 17 and 184 DF,  p-value: 0.8611

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MMP8_rank 
Effect size...............: 0.150568 
Standard error............: 0.340781 
Odds ratio (effect size)..: 1.162 
Lower 95% CI..............: 0.596 
Upper 95% CI..............: 2.267 
T-value...................: 0.441831 
P-value...................: 0.6591302 
R^2.......................: 0.055415 
Adjusted r^2..............: -0.031856 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP9_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -19.612482            0.494020            0.001739  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.0682 -2.2688 -0.9943  0.1890 28.8994 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2.388e+01  1.361e+01  -1.755   0.0810 .
currentDF[, TRAIT]         3.903e-01  3.355e-01   1.163   0.2461  
Age                       -3.156e-02  4.691e-02  -0.673   0.5019  
Gendermale                 5.279e-01  7.885e-01   0.669   0.5040  
ORdate_epoch               1.784e-03  9.011e-04   1.980   0.0492 *
Hypertension.compositeyes  5.669e-01  1.074e+00   0.528   0.5984  
DiabetesStatusDiabetes    -7.052e-01  8.656e-01  -0.815   0.4163  
SmokerStatusEx-smoker      8.623e-01  7.750e-01   1.113   0.2673  
SmokerStatusNever smoked  -1.172e-01  1.019e+00  -0.115   0.9086  
Med.Statin.LLDyes          9.696e-02  7.722e-01   0.126   0.9002  
Med.all.antiplateletyes    7.312e-01  1.212e+00   0.603   0.5470  
GFR_MDRD                  -6.109e-03  1.811e-02  -0.337   0.7363  
BMI                        3.600e-02  8.927e-02   0.403   0.6872  
MedHx_CVDNo               -1.283e-01  7.141e-01  -0.180   0.8576  
stenose50-70%              1.589e+00  5.479e+00   0.290   0.7721  
stenose70-90%              3.888e+00  4.766e+00   0.816   0.4157  
stenose90-99%              3.343e+00  4.745e+00   0.705   0.4820  
stenose100% (Occlusion)    3.520e+00  5.983e+00   0.588   0.5570  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.61 on 184 degrees of freedom
Multiple R-squared:  0.06132,   Adjusted R-squared:  -0.02541 
F-statistic: 0.707 on 17 and 184 DF,  p-value: 0.7933

Analyzing in dataset ' AEDB.CEA ' the association of ' CXCL10 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CXCL10 
Trait/outcome.............: MMP9_rank 
Effect size...............: 0.39034 
Standard error............: 0.335498 
Odds ratio (effect size)..: 1.477 
Lower 95% CI..............: 0.765 
Upper 95% CI..............: 2.852 
T-value...................: 1.163467 
P-value...................: 0.2461467 
R^2.......................: 0.061319 
Adjusted r^2..............: -0.025407 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

Analysis of PCSK9.

- processing IL2_rank
filter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              9.7841230               -0.0007703                1.0501469  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8976 -1.0881 -0.3707  0.4587 11.4538 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.8499630  6.4729132   1.676   0.0958 .
currentDF[, TRAIT]        -0.1604819  0.1672839  -0.959   0.3390  
Age                       -0.0369829  0.0223442  -1.655   0.1000  
Gendermale                 0.0704966  0.4019648   0.175   0.8610  
ORdate_epoch              -0.0006450  0.0004616  -1.397   0.1645  
Hypertension.compositeyes  0.1492126  0.5233539   0.285   0.7760  
DiabetesStatusDiabetes    -0.2708462  0.4385323  -0.618   0.5378  
SmokerStatusEx-smoker      0.5574738  0.3758273   1.483   0.1401  
SmokerStatusNever smoked  -0.0486884  0.5117104  -0.095   0.9243  
Med.Statin.LLDyes         -0.5420890  0.3691959  -1.468   0.1442  
Med.all.antiplateletyes    0.8292273  0.5850324   1.417   0.1585  
GFR_MDRD                  -0.0106803  0.0096009  -1.112   0.2678  
BMI                        0.0097137  0.0498126   0.195   0.8457  
MedHx_CVDNo               -0.2244986  0.3492933  -0.643   0.5214  
stenose70-90%              0.4745161  1.4945174   0.318   0.7513  
stenose90-99%              0.7412788  1.4849468   0.499   0.6184  
stenose100% (Occlusion)    0.0175712  2.1628544   0.008   0.9935  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.993 on 146 degrees of freedom
Multiple R-squared:  0.0971,    Adjusted R-squared:  -0.00185 
F-statistic: 0.9813 on 16 and 146 DF,  p-value: 0.4802

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL2_rank 
Effect size...............: -0.160482 
Standard error............: 0.167284 
Odds ratio (effect size)..: 0.852 
Lower 95% CI..............: 0.614 
Upper 95% CI..............: 1.182 
T-value...................: -0.959339 
P-value...................: 0.3389741 
R^2.......................: 0.097098 
Adjusted r^2..............: -0.00185 
Sample size of AE DB......: 622 
Sample size of model......: 163 
Missing data %............: 73.79421 

- processing IL4_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              9.0634030               -0.0007181                1.1946276  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0907 -1.1247 -0.4529  0.4613 11.2618 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                9.0164852  7.4199541   1.215    0.227
currentDF[, TRAIT]        -0.1152143  0.1915686  -0.601    0.549
Age                       -0.0390202  0.0251083  -1.554    0.123
Gendermale                 0.1287984  0.4485031   0.287    0.774
ORdate_epoch              -0.0005674  0.0005323  -1.066    0.288
Hypertension.compositeyes  0.2452435  0.5891099   0.416    0.678
DiabetesStatusDiabetes    -0.2463283  0.4900267  -0.503    0.616
SmokerStatusEx-smoker      0.5205554  0.4087910   1.273    0.205
SmokerStatusNever smoked   0.0156696  0.5808852   0.027    0.979
Med.Statin.LLDyes         -0.6751317  0.4287609  -1.575    0.118
Med.all.antiplateletyes    1.0021963  0.6435613   1.557    0.122
GFR_MDRD                  -0.0089924  0.0116919  -0.769    0.443
BMI                        0.0311861  0.0585533   0.533    0.595
MedHx_CVDNo               -0.2929116  0.3901139  -0.751    0.454
stenose70-90%              0.6228792  1.5870714   0.392    0.695
stenose90-99%              0.9642027  1.5743899   0.612    0.541
stenose100% (Occlusion)    0.4150523  2.3003823   0.180    0.857

Residual standard error: 2.091 on 128 degrees of freedom
Multiple R-squared:  0.103, Adjusted R-squared:  -0.009104 
F-statistic: 0.9188 on 16 and 128 DF,  p-value: 0.5495

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL4_rank 
Effect size...............: -0.115214 
Standard error............: 0.191569 
Odds ratio (effect size)..: 0.891 
Lower 95% CI..............: 0.612 
Upper 95% CI..............: 1.297 
T-value...................: -0.601426 
P-value...................: 0.5486202 
R^2.......................: 0.103018 
Adjusted r^2..............: -0.009104 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing IL5_rank
filter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
             11.5056670               -0.0009094                1.0911737  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9380 -1.0818 -0.3361  0.5152 11.3968 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               14.3386290  6.7398266   2.127   0.0351 *
currentDF[, TRAIT]        -0.1978491  0.1718405  -1.151   0.2515  
Age                       -0.0395860  0.0237840  -1.664   0.0983 .
Gendermale                 0.1984104  0.4147946   0.478   0.6332  
ORdate_epoch              -0.0008560  0.0004917  -1.741   0.0839 .
Hypertension.compositeyes  0.3002644  0.5496377   0.546   0.5857  
DiabetesStatusDiabetes    -0.2743214  0.4467877  -0.614   0.5402  
SmokerStatusEx-smoker      0.4160766  0.3778558   1.101   0.2727  
SmokerStatusNever smoked  -0.0838780  0.5456915  -0.154   0.8781  
Med.Statin.LLDyes         -0.5945041  0.3887371  -1.529   0.1284  
Med.all.antiplateletyes    0.8481359  0.6021601   1.408   0.1612  
GFR_MDRD                  -0.0089028  0.0103260  -0.862   0.3901  
BMI                        0.0165926  0.0512619   0.324   0.7467  
MedHx_CVDNo               -0.0981801  0.3666731  -0.268   0.7893  
stenose70-90%             -0.7390242  1.2691648  -0.582   0.5613  
stenose90-99%             -0.4162954  1.2536682  -0.332   0.7403  
stenose100% (Occlusion)   -1.2993230  2.0261230  -0.641   0.5224  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.03 on 141 degrees of freedom
Multiple R-squared:  0.1044,    Adjusted R-squared:  0.002754 
F-statistic: 1.027 on 16 and 141 DF,  p-value: 0.4321

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL5_rank 
Effect size...............: -0.197849 
Standard error............: 0.17184 
Odds ratio (effect size)..: 0.82 
Lower 95% CI..............: 0.586 
Upper 95% CI..............: 1.149 
T-value...................: -1.151353 
P-value...................: 0.2515355 
R^2.......................: 0.104384 
Adjusted r^2..............: 0.002754 
Sample size of AE DB......: 622 
Sample size of model......: 158 
Missing data %............: 74.59807 

- processing IL6_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch + 
    Med.all.antiplatelet, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]             ORdate_epoch  Med.all.antiplateletyes  
              8.1938681               -0.2480591               -0.0006334                0.9617455  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5585 -1.1573 -0.3255  0.4134 11.5564 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               10.2721855  6.7266715   1.527    0.129
currentDF[, TRAIT]        -0.2627645  0.1612039  -1.630    0.105
Age                       -0.0356835  0.0235626  -1.514    0.132
Gendermale                 0.0893529  0.3877895   0.230    0.818
ORdate_epoch              -0.0005268  0.0004482  -1.176    0.242
Hypertension.compositeyes  0.2077785  0.5181299   0.401    0.689
DiabetesStatusDiabetes    -0.2356547  0.4335183  -0.544    0.588
SmokerStatusEx-smoker      0.3581933  0.3719235   0.963    0.337
SmokerStatusNever smoked  -0.1742868  0.5160158  -0.338    0.736
Med.Statin.LLDyes         -0.4819758  0.3665816  -1.315    0.191
Med.all.antiplateletyes    0.7741605  0.5944683   1.302    0.195
GFR_MDRD                  -0.0066553  0.0100191  -0.664    0.508
BMI                        0.0172743  0.0429743   0.402    0.688
MedHx_CVDNo               -0.0949186  0.3495195  -0.272    0.786
stenose50-70%             -0.3237433  2.3912171  -0.135    0.892
stenose70-90%             -0.9805087  2.0775112  -0.472    0.638
stenose90-99%             -0.7409162  2.0737036  -0.357    0.721
stenose100% (Occlusion)   -1.4186647  2.6177171  -0.542    0.589

Residual standard error: 1.994 on 146 degrees of freedom
Multiple R-squared:  0.0957,    Adjusted R-squared:  -0.009595 
F-statistic: 0.9089 on 17 and 146 DF,  p-value: 0.5647

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL6_rank 
Effect size...............: -0.262765 
Standard error............: 0.161204 
Odds ratio (effect size)..: 0.769 
Lower 95% CI..............: 0.561 
Upper 95% CI..............: 1.055 
T-value...................: -1.630013 
P-value...................: 0.1052543 
R^2.......................: 0.0957 
Adjusted r^2..............: -0.009595 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL8_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
               9.344728                -0.000721                 1.015887  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0862 -1.1379 -0.3597  0.4404 10.8537 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               11.9641375  7.3986045   1.617    0.108
currentDF[, TRAIT]         0.1605461  0.1788031   0.898    0.371
Age                       -0.0314276  0.0241986  -1.299    0.196
Gendermale                -0.0614816  0.4320821  -0.142    0.887
ORdate_epoch              -0.0006619  0.0004925  -1.344    0.181
Hypertension.compositeyes  0.1522639  0.5359663   0.284    0.777
DiabetesStatusDiabetes    -0.2626711  0.4540220  -0.579    0.564
SmokerStatusEx-smoker      0.4129604  0.4056369   1.018    0.310
SmokerStatusNever smoked  -0.0241556  0.5837892  -0.041    0.967
Med.Statin.LLDyes         -0.3943354  0.3884546  -1.015    0.312
Med.all.antiplateletyes    0.8390684  0.5984857   1.402    0.163
GFR_MDRD                  -0.0113383  0.0095717  -1.185    0.238
BMI                        0.0277434  0.0457343   0.607    0.545
MedHx_CVDNo               -0.1125786  0.3786499  -0.297    0.767
stenose50-70%             -0.2194598  2.4796278  -0.089    0.930
stenose70-90%             -1.2358399  2.1655357  -0.571    0.569
stenose90-99%             -0.8121772  2.1576589  -0.376    0.707
stenose100% (Occlusion)   -1.1218916  3.0804277  -0.364    0.716

Residual standard error: 2.07 on 136 degrees of freedom
Multiple R-squared:  0.08806,   Adjusted R-squared:  -0.02593 
F-statistic: 0.7725 on 17 and 136 DF,  p-value: 0.7217

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL8_rank 
Effect size...............: 0.160546 
Standard error............: 0.178803 
Odds ratio (effect size)..: 1.174 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 1.667 
T-value...................: 0.897894 
P-value...................: 0.3708291 
R^2.......................: 0.088059 
Adjusted r^2..............: -0.025933 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing IL9_rank
filter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.2188013               -0.0006381                1.0688091  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6582 -1.1038 -0.3021  0.4331 10.9431 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                9.9267220  5.7409734   1.729   0.0856 .
currentDF[, TRAIT]         0.0781110  0.1445718   0.540   0.5897  
Age                       -0.0367150  0.0210409  -1.745   0.0828 .
Gendermale                 0.1326587  0.3487967   0.380   0.7042  
ORdate_epoch              -0.0004970  0.0003761  -1.321   0.1882  
Hypertension.compositeyes  0.2726914  0.4709642   0.579   0.5634  
DiabetesStatusDiabetes    -0.1415457  0.3898283  -0.363   0.7170  
SmokerStatusEx-smoker      0.3978122  0.3407456   1.167   0.2447  
SmokerStatusNever smoked  -0.1744844  0.4338981  -0.402   0.6881  
Med.Statin.LLDyes         -0.0938579  0.3445509  -0.272   0.7856  
Med.all.antiplateletyes    0.8769745  0.5696162   1.540   0.1255  
GFR_MDRD                  -0.0069358  0.0079266  -0.875   0.3828  
BMI                        0.0057857  0.0383811   0.151   0.8804  
MedHx_CVDNo               -0.1721271  0.3149396  -0.547   0.5854  
stenose50-70%             -0.0272199  2.4511858  -0.011   0.9912  
stenose70-90%             -1.0184609  1.9863874  -0.513   0.6088  
stenose90-99%             -0.7429599  1.9780207  -0.376   0.7077  
stenose100% (Occlusion)   -1.4251375  2.5160945  -0.566   0.5719  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.921 on 168 degrees of freedom
Multiple R-squared:  0.08586,   Adjusted R-squared:  -0.006646 
F-statistic: 0.9282 on 17 and 168 DF,  p-value: 0.5421

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL9_rank 
Effect size...............: 0.078111 
Standard error............: 0.144572 
Odds ratio (effect size)..: 1.081 
Lower 95% CI..............: 0.814 
Upper 95% CI..............: 1.435 
T-value...................: 0.540292 
P-value...................: 0.5897109 
R^2.......................: 0.085857 
Adjusted r^2..............: -0.006646 
Sample size of AE DB......: 622 
Sample size of model......: 186 
Missing data %............: 70.09646 

- processing IL10_rank
filter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
             10.1892459               -0.0008074                1.1598776  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0236 -1.1934 -0.5005  0.5369 11.2467 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)
(Intercept)               10.447421   8.117580   1.287    0.201
currentDF[, TRAIT]        -0.131923   0.197096  -0.669    0.505
Age                       -0.038889   0.026484  -1.468    0.145
Gendermale                 0.065425   0.466762   0.140    0.889
ORdate_epoch              -0.000708   0.000568  -1.246    0.215
Hypertension.compositeyes  0.105998   0.617114   0.172    0.864
DiabetesStatusDiabetes    -0.233209   0.509791  -0.457    0.648
SmokerStatusEx-smoker      0.540133   0.430895   1.254    0.212
SmokerStatusNever smoked   0.166542   0.599660   0.278    0.782
Med.Statin.LLDyes         -0.616848   0.429729  -1.435    0.154
Med.all.antiplateletyes    0.884548   0.668052   1.324    0.188
GFR_MDRD                  -0.008202   0.012248  -0.670    0.504
BMI                        0.046213   0.060507   0.764    0.446
MedHx_CVDNo               -0.315055   0.422730  -0.745    0.458
stenose70-90%              0.602006   1.630495   0.369    0.713
stenose90-99%              0.993206   1.617475   0.614    0.540
stenose100% (Occlusion)    0.366245   2.365797   0.155    0.877

Residual standard error: 2.142 on 122 degrees of freedom
Multiple R-squared:  0.1002,    Adjusted R-squared:  -0.01778 
F-statistic: 0.8493 on 16 and 122 DF,  p-value: 0.6278

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL10_rank 
Effect size...............: -0.131923 
Standard error............: 0.197096 
Odds ratio (effect size)..: 0.876 
Lower 95% CI..............: 0.596 
Upper 95% CI..............: 1.29 
T-value...................: -0.669337 
P-value...................: 0.5045452 
R^2.......................: 0.100225 
Adjusted r^2..............: -0.017779 
Sample size of AE DB......: 622 
Sample size of model......: 139 
Missing data %............: 77.65273 

- processing IL12_rank
filter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
             10.0968678               -0.0007946                1.0821656  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1174 -1.1352 -0.4365  0.5206 11.1735 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               10.1922214  7.7219594   1.320    0.189
currentDF[, TRAIT]        -0.0808842  0.1878581  -0.431    0.668
Age                       -0.0405068  0.0259160  -1.563    0.121
Gendermale                 0.1384201  0.4442982   0.312    0.756
ORdate_epoch              -0.0006468  0.0005377  -1.203    0.231
Hypertension.compositeyes  0.0224375  0.6042902   0.037    0.970
DiabetesStatusDiabetes    -0.2632880  0.4814394  -0.547    0.585
SmokerStatusEx-smoker      0.5484000  0.4272637   1.284    0.202
SmokerStatusNever smoked  -0.0077124  0.5669372  -0.014    0.989
Med.Statin.LLDyes         -0.6296595  0.4252754  -1.481    0.141
Med.all.antiplateletyes    0.9202621  0.6463561   1.424    0.157
GFR_MDRD                  -0.0095510  0.0114392  -0.835    0.405
BMI                        0.0336885  0.0578831   0.582    0.562
MedHx_CVDNo               -0.2831087  0.3899297  -0.726    0.469
stenose70-90%              0.7150757  1.5827339   0.452    0.652
stenose90-99%              1.0531050  1.5764104   0.668    0.505
stenose100% (Occlusion)    1.0033002  2.7732041   0.362    0.718

Residual standard error: 2.09 on 129 degrees of freedom
Multiple R-squared:  0.1005,    Adjusted R-squared:  -0.01112 
F-statistic: 0.9004 on 16 and 129 DF,  p-value: 0.5702

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL12_rank 
Effect size...............: -0.080884 
Standard error............: 0.187858 
Odds ratio (effect size)..: 0.922 
Lower 95% CI..............: 0.638 
Upper 95% CI..............: 1.333 
T-value...................: -0.43056 
P-value...................: 0.6675067 
R^2.......................: 0.100455 
Adjusted r^2..............: -0.011117 
Sample size of AE DB......: 622 
Sample size of model......: 146 
Missing data %............: 76.52733 

- processing IL13_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.4803387               -0.0006436                0.8802809  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5061 -1.1357 -0.3728  0.4299 11.0052 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.1066481  5.5578923   1.818   0.0706 .
currentDF[, TRAIT]         0.0224575  0.1431783   0.157   0.8755  
Age                       -0.0302615  0.0197652  -1.531   0.1275  
Gendermale                 0.0503174  0.3315109   0.152   0.8795  
ORdate_epoch              -0.0005338  0.0003687  -1.448   0.1494  
Hypertension.compositeyes  0.2063253  0.4328991   0.477   0.6342  
DiabetesStatusDiabetes    -0.1729794  0.3583946  -0.483   0.6299  
SmokerStatusEx-smoker      0.3257533  0.3233488   1.007   0.3150  
SmokerStatusNever smoked  -0.3036498  0.4231855  -0.718   0.4740  
Med.Statin.LLDyes         -0.1688331  0.3214724  -0.525   0.6001  
Med.all.antiplateletyes    0.6365460  0.5005834   1.272   0.2051  
GFR_MDRD                  -0.0080800  0.0076631  -1.054   0.2931  
BMI                        0.0191669  0.0358691   0.534   0.5937  
MedHx_CVDNo                0.0304524  0.2930571   0.104   0.9174  
stenose50-70%             -0.3756820  2.2717042  -0.165   0.8688  
stenose70-90%             -1.0068799  1.9666696  -0.512   0.6093  
stenose90-99%             -0.7803267  1.9644597  -0.397   0.6917  
stenose100% (Occlusion)   -1.6568699  2.4628016  -0.673   0.5019  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared:  0.07074,   Adjusted R-squared:  -0.01511 
F-statistic: 0.824 on 17 and 184 DF,  p-value: 0.6641

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL13_rank 
Effect size...............: 0.022458 
Standard error............: 0.143178 
Odds ratio (effect size)..: 1.023 
Lower 95% CI..............: 0.772 
Upper 95% CI..............: 1.354 
T-value...................: 0.15685 
P-value...................: 0.875535 
R^2.......................: 0.070743 
Adjusted r^2..............: -0.015113 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing IL21_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.4803387               -0.0006436                0.8802809  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5199 -1.1331 -0.3898  0.4497 11.0197 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.1722243  5.5585042   1.830   0.0689 .
currentDF[, TRAIT]         0.0447558  0.1420252   0.315   0.7530  
Age                       -0.0299205  0.0195853  -1.528   0.1283  
Gendermale                 0.0433904  0.3320499   0.131   0.8962  
ORdate_epoch              -0.0005376  0.0003679  -1.461   0.1457  
Hypertension.compositeyes  0.2069357  0.4317194   0.479   0.6323  
DiabetesStatusDiabetes    -0.1697638  0.3578088  -0.474   0.6357  
SmokerStatusEx-smoker      0.3221765  0.3213386   1.003   0.3174  
SmokerStatusNever smoked  -0.3139364  0.4212443  -0.745   0.4571  
Med.Statin.LLDyes         -0.1746116  0.3219016  -0.542   0.5882  
Med.all.antiplateletyes    0.6315310  0.5007826   1.261   0.2089  
GFR_MDRD                  -0.0081156  0.0076559  -1.060   0.2905  
BMI                        0.0191494  0.0358317   0.534   0.5937  
MedHx_CVDNo                0.0322806  0.2928154   0.110   0.9123  
stenose50-70%             -0.4006546  2.2685660  -0.177   0.8600  
stenose70-90%             -1.0250053  1.9653242  -0.522   0.6026  
stenose90-99%             -0.8083071  1.9630100  -0.412   0.6810  
stenose100% (Occlusion)   -1.6713931  2.4626962  -0.679   0.4982  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared:  0.07112,   Adjusted R-squared:  -0.0147 
F-statistic: 0.8287 on 17 and 184 DF,  p-value: 0.6586

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IL21_rank 
Effect size...............: 0.044756 
Standard error............: 0.142025 
Odds ratio (effect size)..: 1.046 
Lower 95% CI..............: 0.792 
Upper 95% CI..............: 1.381 
T-value...................: 0.315126 
P-value...................: 0.7530234 
R^2.......................: 0.07112 
Adjusted r^2..............: -0.014701 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing INFG_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
             10.0315552               -0.0007856                1.0670223  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1496 -1.1159 -0.3970  0.4229 11.1084 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               13.4174786  7.4306247   1.806   0.0732 .
currentDF[, TRAIT]        -0.0557169  0.1923920  -0.290   0.7726  
Age                       -0.0389797  0.0244923  -1.592   0.1138  
Gendermale                 0.0622839  0.4470232   0.139   0.8894  
ORdate_epoch              -0.0007333  0.0005027  -1.459   0.1469  
Hypertension.compositeyes  0.1269517  0.5920853   0.214   0.8305  
DiabetesStatusDiabetes    -0.3980255  0.4496163  -0.885   0.3776  
SmokerStatusEx-smoker      0.5325232  0.4049553   1.315   0.1907  
SmokerStatusNever smoked   0.0214474  0.5505104   0.039   0.9690  
Med.Statin.LLDyes         -0.5832413  0.4002952  -1.457   0.1474  
Med.all.antiplateletyes    0.8618680  0.5867285   1.469   0.1442  
GFR_MDRD                  -0.0130252  0.0098962  -1.316   0.1903  
BMI                        0.0279973  0.0457195   0.612   0.5413  
MedHx_CVDNo               -0.1992049  0.3859411  -0.516   0.6066  
stenose50-70%             -0.2654932  2.4776648  -0.107   0.9148  
stenose70-90%             -1.1291594  2.1615671  -0.522   0.6023  
stenose90-99%             -0.8469573  2.1495716  -0.394   0.6942  
stenose100% (Occlusion)   -0.9177005  3.0548773  -0.300   0.7643  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.062 on 136 degrees of freedom
Multiple R-squared:  0.1026,    Adjusted R-squared:  -0.009566 
F-statistic: 0.9147 on 17 and 136 DF,  p-value: 0.5581

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: INFG_rank 
Effect size...............: -0.055717 
Standard error............: 0.192392 
Odds ratio (effect size)..: 0.946 
Lower 95% CI..............: 0.649 
Upper 95% CI..............: 1.379 
T-value...................: -0.289601 
P-value...................: 0.7725627 
R^2.......................: 0.102608 
Adjusted r^2..............: -0.009566 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing TNFA_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
             10.1044919               -0.0007999                1.1605469  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0884 -1.1572 -0.4480  0.5446 11.2659 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               11.0223039  7.4681209   1.476    0.142
currentDF[, TRAIT]        -0.1427858  0.1878069  -0.760    0.448
Age                       -0.0364955  0.0253706  -1.438    0.153
Gendermale                 0.0961476  0.4431679   0.217    0.829
ORdate_epoch              -0.0007416  0.0005204  -1.425    0.157
Hypertension.compositeyes  0.1604431  0.5939506   0.270    0.787
DiabetesStatusDiabetes    -0.2403900  0.4795920  -0.501    0.617
SmokerStatusEx-smoker      0.5923820  0.4134725   1.433    0.154
SmokerStatusNever smoked   0.0015506  0.5727048   0.003    0.998
Med.Statin.LLDyes         -0.5833048  0.4259811  -1.369    0.173
Med.all.antiplateletyes    0.9399976  0.6393322   1.470    0.144
GFR_MDRD                  -0.0085052  0.0110162  -0.772    0.441
BMI                        0.0314197  0.0545572   0.576    0.566
MedHx_CVDNo               -0.3603229  0.4009648  -0.899    0.371
stenose70-90%              0.6105518  1.5839803   0.385    0.701
stenose90-99%              0.9857141  1.5678929   0.629    0.531
stenose100% (Occlusion)    1.0378852  2.7626016   0.376    0.708

Residual standard error: 2.094 on 128 degrees of freedom
Multiple R-squared:  0.1047,    Adjusted R-squared:  -0.007222 
F-statistic: 0.9355 on 16 and 128 DF,  p-value: 0.531

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: TNFA_rank 
Effect size...............: -0.142786 
Standard error............: 0.187807 
Odds ratio (effect size)..: 0.867 
Lower 95% CI..............: 0.6 
Upper 95% CI..............: 1.253 
T-value...................: -0.76028 
P-value...................: 0.4484856 
R^2.......................: 0.104691 
Adjusted r^2..............: -0.007222 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing MIF_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]  Med.all.antiplateletyes  
                 0.5204                   0.2758                   0.7506  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3556 -1.1251 -0.3760  0.5035 10.9184 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                6.9762733  5.9860953   1.165    0.245
currentDF[, TRAIT]         0.2174944  0.1638785   1.327    0.186
Age                       -0.0287745  0.0193167  -1.490    0.138
Gendermale                 0.0338784  0.3287757   0.103    0.918
ORdate_epoch              -0.0002809  0.0004096  -0.686    0.494
Hypertension.compositeyes  0.2672490  0.4322416   0.618    0.537
DiabetesStatusDiabetes    -0.1471299  0.3559940  -0.413    0.680
SmokerStatusEx-smoker      0.3642270  0.3186260   1.143    0.254
SmokerStatusNever smoked  -0.3442418  0.4146906  -0.830    0.408
Med.Statin.LLDyes         -0.1854076  0.3193536  -0.581    0.562
Med.all.antiplateletyes    0.5830819  0.4997333   1.167    0.245
GFR_MDRD                  -0.0063868  0.0077334  -0.826    0.410
BMI                        0.0175248  0.0356779   0.491    0.624
MedHx_CVDNo                0.0513931  0.2916814   0.176    0.860
stenose50-70%             -0.5573581  2.2565292  -0.247    0.805
stenose70-90%             -1.2104885  1.9597502  -0.618    0.538
stenose90-99%             -1.0169215  1.9557079  -0.520    0.604
stenose100% (Occlusion)   -1.8147752  2.4541232  -0.739    0.461

Residual standard error: 1.894 on 184 degrees of freedom
Multiple R-squared:  0.07943,   Adjusted R-squared:  -0.005622 
F-statistic: 0.9339 on 17 and 184 DF,  p-value: 0.5352

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MIF_rank 
Effect size...............: 0.217494 
Standard error............: 0.163878 
Odds ratio (effect size)..: 1.243 
Lower 95% CI..............: 0.901 
Upper 95% CI..............: 1.714 
T-value...................: 1.327169 
P-value...................: 0.1860974 
R^2.......................: 0.079431 
Adjusted r^2..............: -0.005622 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MCP1_rank
filter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
               8.599177                -0.000648                 0.816759  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5407 -1.0998 -0.3813  0.4674 11.0932 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                9.3614211  5.7345598   1.632    0.104
currentDF[, TRAIT]         0.0677775  0.1411637   0.480    0.632
Age                       -0.0286516  0.0197067  -1.454    0.148
Gendermale                 0.0362668  0.3360299   0.108    0.914
ORdate_epoch              -0.0004824  0.0003795  -1.271    0.205
Hypertension.compositeyes  0.2252893  0.4369964   0.516    0.607
DiabetesStatusDiabetes    -0.1735715  0.3604913  -0.481    0.631
SmokerStatusEx-smoker      0.3300709  0.3217286   1.026    0.306
SmokerStatusNever smoked  -0.3245413  0.4213890  -0.770    0.442
Med.Statin.LLDyes         -0.1673267  0.3238821  -0.517    0.606
Med.all.antiplateletyes    0.5419435  0.5335727   1.016    0.311
GFR_MDRD                  -0.0082400  0.0077276  -1.066    0.288
BMI                        0.0220328  0.0365758   0.602    0.548
MedHx_CVDNo                0.0245129  0.2958709   0.083    0.934
stenose50-70%             -0.3402382  2.2712261  -0.150    0.881
stenose70-90%             -1.0053285  1.9703958  -0.510    0.611
stenose90-99%             -0.7581317  1.9635063  -0.386    0.700
stenose100% (Occlusion)   -1.6711974  2.4809516  -0.674    0.501

Residual standard error: 1.91 on 182 degrees of freedom
Multiple R-squared:  0.0695,    Adjusted R-squared:  -0.01742 
F-statistic: 0.7996 on 17 and 182 DF,  p-value: 0.6922

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MCP1_rank 
Effect size...............: 0.067777 
Standard error............: 0.141164 
Odds ratio (effect size)..: 1.07 
Lower 95% CI..............: 0.811 
Upper 95% CI..............: 1.411 
T-value...................: 0.480134 
P-value...................: 0.6317087 
R^2.......................: 0.069499 
Adjusted r^2..............: -0.017416 
Sample size of AE DB......: 622 
Sample size of model......: 200 
Missing data %............: 67.84566 

- processing MIP1a_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              7.9495652               -0.0006165                1.0482385  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5313 -1.0918 -0.2972  0.3988 10.9669 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                9.6993732  5.6629973   1.713   0.0886 .
currentDF[, TRAIT]         0.0729062  0.1453901   0.501   0.6167  
Age                       -0.0343749  0.0201529  -1.706   0.0899 .
Gendermale                 0.1100657  0.3481968   0.316   0.7523  
ORdate_epoch              -0.0004939  0.0003737  -1.322   0.1881  
Hypertension.compositeyes  0.2834033  0.4561234   0.621   0.5352  
DiabetesStatusDiabetes    -0.2087618  0.3760301  -0.555   0.5795  
SmokerStatusEx-smoker      0.3904305  0.3369778   1.159   0.2482  
SmokerStatusNever smoked  -0.1736360  0.4329765  -0.401   0.6889  
Med.Statin.LLDyes         -0.0275524  0.3363561  -0.082   0.9348  
Med.all.antiplateletyes    0.8170591  0.5646249   1.447   0.1497  
GFR_MDRD                  -0.0073824  0.0078618  -0.939   0.3490  
BMI                        0.0097185  0.0372298   0.261   0.7944  
MedHx_CVDNo               -0.1389726  0.3122150  -0.445   0.6568  
stenose50-70%              0.0292979  2.4485865   0.012   0.9905  
stenose70-90%             -1.0575102  1.9827740  -0.533   0.5945  
stenose90-99%             -0.7909050  1.9795485  -0.400   0.6900  
stenose100% (Occlusion)   -1.4730931  2.4965846  -0.590   0.5559  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.912 on 171 degrees of freedom
Multiple R-squared:  0.08263,   Adjusted R-squared:  -0.008569 
F-statistic: 0.906 on 17 and 171 DF,  p-value: 0.5678

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 0.072906 
Standard error............: 0.14539 
Odds ratio (effect size)..: 1.076 
Lower 95% CI..............: 0.809 
Upper 95% CI..............: 1.43 
T-value...................: 0.501452 
P-value...................: 0.6166979 
R^2.......................: 0.082632 
Adjusted r^2..............: -0.008569 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing RANTES_rank
filter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.3580186               -0.0006332                0.8650319  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4977 -1.1406 -0.3838  0.4244 11.0214 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                9.6913048  5.9698422   1.623    0.106
currentDF[, TRAIT]         0.0140825  0.1566373   0.090    0.928
Age                       -0.0304608  0.0199360  -1.528    0.128
Gendermale                 0.0387236  0.3410783   0.114    0.910
ORdate_epoch              -0.0004879  0.0004025  -1.212    0.227
Hypertension.compositeyes  0.1733835  0.4523464   0.383    0.702
DiabetesStatusDiabetes    -0.1606232  0.3687753  -0.436    0.664
SmokerStatusEx-smoker      0.3878841  0.3290352   1.179    0.240
SmokerStatusNever smoked  -0.2354760  0.4255011  -0.553    0.581
Med.Statin.LLDyes         -0.1633726  0.3264400  -0.500    0.617
Med.all.antiplateletyes    0.6457597  0.5325051   1.213    0.227
GFR_MDRD                  -0.0083758  0.0077818  -1.076    0.283
BMI                        0.0146513  0.0367566   0.399    0.691
MedHx_CVDNo               -0.0201064  0.3052225  -0.066    0.948
stenose50-70%              0.0124319  2.4354595   0.005    0.996
stenose70-90%             -1.0439219  1.9868430  -0.525    0.600
stenose90-99%             -0.7814923  1.9768063  -0.395    0.693
stenose100% (Occlusion)   -1.6900568  2.5056835  -0.674    0.501

Residual standard error: 1.918 on 180 degrees of freedom
Multiple R-squared:  0.06993,   Adjusted R-squared:  -0.01791 
F-statistic: 0.7962 on 17 and 180 DF,  p-value: 0.6962

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: RANTES_rank 
Effect size...............: 0.014083 
Standard error............: 0.156637 
Odds ratio (effect size)..: 1.014 
Lower 95% CI..............: 0.746 
Upper 95% CI..............: 1.379 
T-value...................: 0.089905 
P-value...................: 0.9284623 
R^2.......................: 0.069934 
Adjusted r^2..............: -0.017905 
Sample size of AE DB......: 622 
Sample size of model......: 198 
Missing data %............: 68.1672 

- processing MIG_rank
filter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.3478925               -0.0006349                0.8846570  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5195 -1.1005 -0.3751  0.4546 11.0365 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.5299371  5.7497474   1.831   0.0687 .
currentDF[, TRAIT]         0.0865596  0.1546555   0.560   0.5764  
Age                       -0.0286966  0.0197136  -1.456   0.1472  
Gendermale                 0.0441741  0.3345768   0.132   0.8951  
ORdate_epoch              -0.0005661  0.0003867  -1.464   0.1450  
Hypertension.compositeyes  0.1692077  0.4435762   0.381   0.7033  
DiabetesStatusDiabetes    -0.2744302  0.3628554  -0.756   0.4504  
SmokerStatusEx-smoker      0.2822934  0.3245896   0.870   0.3856  
SmokerStatusNever smoked  -0.3002840  0.4247399  -0.707   0.4805  
Med.Statin.LLDyes         -0.0998566  0.3246399  -0.308   0.7587  
Med.all.antiplateletyes    0.6301254  0.5102685   1.235   0.2185  
GFR_MDRD                  -0.0093893  0.0076666  -1.225   0.2223  
BMI                        0.0180473  0.0358039   0.504   0.6148  
MedHx_CVDNo               -0.0437043  0.2971674  -0.147   0.8832  
stenose50-70%              0.0306047  2.4236749   0.013   0.9899  
stenose70-90%             -0.9446925  1.9602889  -0.482   0.6304  
stenose90-99%             -0.7699762  1.9558058  -0.394   0.6943  
stenose100% (Occlusion)   -1.5706709  2.4629317  -0.638   0.5245  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.896 on 181 degrees of freedom
Multiple R-squared:  0.07296,   Adjusted R-squared:  -0.01411 
F-statistic: 0.838 on 17 and 181 DF,  p-value: 0.6477

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MIG_rank 
Effect size...............: 0.08656 
Standard error............: 0.154656 
Odds ratio (effect size)..: 1.09 
Lower 95% CI..............: 0.805 
Upper 95% CI..............: 1.477 
T-value...................: 0.559693 
P-value...................: 0.5763809 
R^2.......................: 0.072961 
Adjusted r^2..............: -0.014109 
Sample size of AE DB......: 622 
Sample size of model......: 199 
Missing data %............: 68.00643 

- processing IP10_rank
filter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)                      Age             ORdate_epoch  Med.all.antiplateletyes  
              8.7775730               -0.0251532               -0.0005465                1.0418070  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2665 -1.1059 -0.3324  0.5272 10.8196 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                8.7258331  5.6923572   1.533   0.1272  
currentDF[, TRAIT]        -0.0137094  0.1523988  -0.090   0.9284  
Age                       -0.0385512  0.0207998  -1.853   0.0656 .
Gendermale                 0.1354236  0.3414521   0.397   0.6922  
ORdate_epoch              -0.0004052  0.0003745  -1.082   0.2808  
Hypertension.compositeyes  0.1808014  0.4622322   0.391   0.6962  
DiabetesStatusDiabetes    -0.3462517  0.3872862  -0.894   0.3726  
SmokerStatusEx-smoker      0.3554329  0.3446304   1.031   0.3039  
SmokerStatusNever smoked  -0.0966596  0.4438970  -0.218   0.8279  
Med.Statin.LLDyes          0.1079288  0.3417310   0.316   0.7525  
Med.all.antiplateletyes    0.7758466  0.5292407   1.466   0.1446  
GFR_MDRD                  -0.0055941  0.0081258  -0.688   0.4921  
BMI                        0.0050427  0.0368020   0.137   0.8912  
MedHx_CVDNo               -0.1177216  0.3128021  -0.376   0.7071  
stenose50-70%              0.2623824  2.4337011   0.108   0.9143  
stenose70-90%             -0.9234660  1.9747508  -0.468   0.6407  
stenose90-99%             -0.5665642  1.9635420  -0.289   0.7733  
stenose100% (Occlusion)   -1.5214714  2.4804733  -0.613   0.5405  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.903 on 165 degrees of freedom
Multiple R-squared:  0.08232,   Adjusted R-squared:  -0.01223 
F-statistic: 0.8707 on 17 and 165 DF,  p-value: 0.6093

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: IP10_rank 
Effect size...............: -0.013709 
Standard error............: 0.152399 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.732 
Upper 95% CI..............: 1.33 
T-value...................: -0.089958 
P-value...................: 0.92843 
R^2.......................: 0.082322 
Adjusted r^2..............: -0.012227 
Sample size of AE DB......: 622 
Sample size of model......: 183 
Missing data %............: 70.57878 

- processing Eotaxin1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.4803387               -0.0006436                0.8802809  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5582 -1.1404 -0.3850  0.4638 10.9961 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.3587629  5.6192629   1.843   0.0669 .
currentDF[, TRAIT]         0.0488035  0.1441032   0.339   0.7352  
Age                       -0.0299937  0.0195206  -1.537   0.1261  
Gendermale                 0.0412188  0.3324891   0.124   0.9015  
ORdate_epoch              -0.0005519  0.0003734  -1.478   0.1412  
Hypertension.compositeyes  0.1999904  0.4312289   0.464   0.6434  
DiabetesStatusDiabetes    -0.1689941  0.3578262  -0.472   0.6373  
SmokerStatusEx-smoker      0.3226776  0.3208888   1.006   0.3159  
SmokerStatusNever smoked  -0.3149659  0.4208498  -0.748   0.4552  
Med.Statin.LLDyes         -0.1713812  0.3209774  -0.534   0.5940  
Med.all.antiplateletyes    0.6292792  0.5010285   1.256   0.2107  
GFR_MDRD                  -0.0080675  0.0076575  -1.054   0.2935  
BMI                        0.0193759  0.0358487   0.540   0.5895  
MedHx_CVDNo                0.0337397  0.2929871   0.115   0.9084  
stenose50-70%             -0.3928100  2.2657659  -0.173   0.8626  
stenose70-90%             -1.0285994  1.9653541  -0.523   0.6013  
stenose90-99%             -0.8139391  1.9632756  -0.415   0.6789  
stenose100% (Occlusion)   -1.6961631  2.4648471  -0.688   0.4922  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared:  0.0712,    Adjusted R-squared:  -0.01462 
F-statistic: 0.8297 on 17 and 184 DF,  p-value: 0.6575

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 0.048803 
Standard error............: 0.144103 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.792 
Upper 95% CI..............: 1.393 
T-value...................: 0.33867 
P-value...................: 0.7352444 
R^2.......................: 0.071197 
Adjusted r^2..............: -0.014616 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing TARC_rank
filter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerStatus + 
    Med.all.antiplatelet, data = currentDF)

Coefficients:
             (Intercept)        currentDF[, TRAIT]     SmokerStatusEx-smoker  SmokerStatusNever smoked   Med.all.antiplateletyes  
                  0.2120                    0.2046                    0.5367                   -0.1471                    0.7674  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4440 -1.0432 -0.4210  0.4805  8.4707 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                3.1143480  6.1290244   0.508    0.612
currentDF[, TRAIT]         0.1695653  0.1467620   1.155    0.250
Age                       -0.0077344  0.0194097  -0.398    0.691
Gendermale                 0.4173436  0.3271825   1.276    0.204
ORdate_epoch              -0.0001618  0.0004251  -0.381    0.704
Hypertension.compositeyes  0.0349649  0.4124053   0.085    0.933
DiabetesStatusDiabetes    -0.1697568  0.3452465  -0.492    0.624
SmokerStatusEx-smoker      0.4468678  0.3132238   1.427    0.156
SmokerStatusNever smoked  -0.1801166  0.4002199  -0.450    0.653
Med.Statin.LLDyes         -0.2676390  0.3257350  -0.822    0.413
Med.all.antiplateletyes    0.8028104  0.5048335   1.590    0.114
GFR_MDRD                  -0.0061895  0.0075594  -0.819    0.414
BMI                        0.0300940  0.0349072   0.862    0.390
MedHx_CVDNo               -0.0384266  0.2874806  -0.134    0.894
stenose50-70%             -1.5676876  2.2073263  -0.710    0.479
stenose70-90%             -0.8313932  1.8035654  -0.461    0.645
stenose90-99%             -0.7803866  1.7975315  -0.434    0.665
stenose100% (Occlusion)   -1.1192487  2.2889690  -0.489    0.626

Residual standard error: 1.743 on 160 degrees of freedom
Multiple R-squared:  0.08478,   Adjusted R-squared:  -0.01246 
F-statistic: 0.8719 on 17 and 160 DF,  p-value: 0.6078

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: TARC_rank 
Effect size...............: 0.169565 
Standard error............: 0.146762 
Odds ratio (effect size)..: 1.185 
Lower 95% CI..............: 0.889 
Upper 95% CI..............: 1.58 
T-value...................: 1.155377 
P-value...................: 0.2496587 
R^2.......................: 0.084784 
Adjusted r^2..............: -0.012457 
Sample size of AE DB......: 622 
Sample size of model......: 178 
Missing data %............: 71.38264 

- processing PARC_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.4803387               -0.0006436                0.8802809  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4010 -1.1118 -0.3694  0.3679 11.1375 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                8.5825893  5.9897115   1.433    0.154
currentDF[, TRAIT]         0.0981998  0.1523957   0.644    0.520
Age                       -0.0297714  0.0193996  -1.535    0.127
Gendermale                 0.0624007  0.3297507   0.189    0.850
ORdate_epoch              -0.0004282  0.0003975  -1.077    0.283
Hypertension.compositeyes  0.2440031  0.4361189   0.559    0.577
DiabetesStatusDiabetes    -0.1634999  0.3572421  -0.458    0.648
SmokerStatusEx-smoker      0.3255648  0.3192092   1.020    0.309
SmokerStatusNever smoked  -0.3324338  0.4193134  -0.793    0.429
Med.Statin.LLDyes         -0.1741226  0.3204623  -0.543    0.588
Med.all.antiplateletyes    0.6588062  0.5006684   1.316    0.190
GFR_MDRD                  -0.0074872  0.0077133  -0.971    0.333
BMI                        0.0213084  0.0359863   0.592    0.554
MedHx_CVDNo                0.0209283  0.2923763   0.072    0.943
stenose50-70%             -0.3424317  2.2588627  -0.152    0.880
stenose70-90%             -1.0088516  1.9598196  -0.515    0.607
stenose90-99%             -0.7605182  1.9524453  -0.390    0.697
stenose100% (Occlusion)   -1.5343122  2.4676007  -0.622    0.535

Residual standard error: 1.9 on 184 degrees of freedom
Multiple R-squared:  0.07271,   Adjusted R-squared:  -0.01296 
F-statistic: 0.8487 on 17 and 184 DF,  p-value: 0.6352

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: PARC_rank 
Effect size...............: 0.0982 
Standard error............: 0.152396 
Odds ratio (effect size)..: 1.103 
Lower 95% CI..............: 0.818 
Upper 95% CI..............: 1.487 
T-value...................: 0.644374 
P-value...................: 0.5201355 
R^2.......................: 0.072711 
Adjusted r^2..............: -0.012963 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MDC_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]  Med.all.antiplateletyes  
                 0.2144                   0.2812                   1.0225  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7140 -1.0693 -0.2813  0.3677 11.0069 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                7.6874430  5.8064844   1.324    0.187
currentDF[, TRAIT]         0.2077752  0.1526293   1.361    0.175
Age                       -0.0330479  0.0200043  -1.652    0.100
Gendermale                 0.1092927  0.3443970   0.317    0.751
ORdate_epoch              -0.0003392  0.0003876  -0.875    0.383
Hypertension.compositeyes  0.2899076  0.4538702   0.639    0.524
DiabetesStatusDiabetes    -0.1643375  0.3775292  -0.435    0.664
SmokerStatusEx-smoker      0.4163006  0.3337993   1.247    0.214
SmokerStatusNever smoked  -0.2102146  0.4297679  -0.489    0.625
Med.Statin.LLDyes         -0.0937902  0.3386598  -0.277    0.782
Med.all.antiplateletyes    0.8739682  0.5635205   1.551    0.123
GFR_MDRD                  -0.0071842  0.0077959  -0.922    0.358
BMI                        0.0109650  0.0372457   0.294    0.769
MedHx_CVDNo               -0.1244947  0.3107730  -0.401    0.689
stenose50-70%             -0.2400355  2.4393169  -0.098    0.922
stenose70-90%             -1.1481277  1.9707002  -0.583    0.561
stenose90-99%             -0.9006567  1.9639923  -0.459    0.647
stenose100% (Occlusion)   -1.4665704  2.4836216  -0.590    0.556

Residual standard error: 1.903 on 171 degrees of freedom
Multiple R-squared:  0.09242,   Adjusted R-squared:  0.002194 
F-statistic: 1.024 on 17 and 171 DF,  p-value: 0.4344

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MDC_rank 
Effect size...............: 0.207775 
Standard error............: 0.152629 
Odds ratio (effect size)..: 1.231 
Lower 95% CI..............: 0.913 
Upper 95% CI..............: 1.66 
T-value...................: 1.361306 
P-value...................: 0.1752081 
R^2.......................: 0.092421 
Adjusted r^2..............: 0.002194 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing OPG_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.4803387               -0.0006436                0.8802809  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.3823 -1.0481 -0.4359  0.4309 10.7292 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.3746459  5.5441010   1.871   0.0629 .
currentDF[, TRAIT]        -0.1297792  0.1386467  -0.936   0.3505  
Age                       -0.0339936  0.0195771  -1.736   0.0842 .
Gendermale                 0.0745380  0.3297948   0.226   0.8214  
ORdate_epoch              -0.0005346  0.0003659  -1.461   0.1457  
Hypertension.compositeyes  0.1660542  0.4319120   0.384   0.7011  
DiabetesStatusDiabetes    -0.1868259  0.3561992  -0.524   0.6006  
SmokerStatusEx-smoker      0.3761292  0.3217576   1.169   0.2439  
SmokerStatusNever smoked  -0.2367943  0.4176290  -0.567   0.5714  
Med.Statin.LLDyes         -0.1498216  0.3201456  -0.468   0.6404  
Med.all.antiplateletyes    0.6829027  0.5012981   1.362   0.1748  
GFR_MDRD                  -0.0081278  0.0076397  -1.064   0.2888  
BMI                        0.0159704  0.0358854   0.445   0.6568  
MedHx_CVDNo                0.0291722  0.2918318   0.100   0.9205  
stenose50-70%             -0.2882399  2.2567633  -0.128   0.8985  
stenose70-90%             -1.0087584  1.9571841  -0.515   0.6069  
stenose90-99%             -0.7502996  1.9499317  -0.385   0.7008  
stenose100% (Occlusion)   -1.8388629  2.4647512  -0.746   0.4566  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.898 on 184 degrees of freedom
Multiple R-squared:  0.07502,   Adjusted R-squared:  -0.01044 
F-statistic: 0.8779 on 17 and 184 DF,  p-value: 0.6008

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: OPG_rank 
Effect size...............: -0.129779 
Standard error............: 0.138647 
Odds ratio (effect size)..: 0.878 
Lower 95% CI..............: 0.669 
Upper 95% CI..............: 1.153 
T-value...................: -0.936042 
P-value...................: 0.3504783 
R^2.......................: 0.075023 
Adjusted r^2..............: -0.010437 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing sICAM1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.4803387               -0.0006436                0.8802809  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5245 -1.0992 -0.3678  0.4598 11.0038 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                9.4761057  5.6929815   1.665   0.0977 .
currentDF[, TRAIT]         0.0646077  0.1460203   0.442   0.6587  
Age                       -0.0291581  0.0197288  -1.478   0.1411  
Gendermale                 0.0502699  0.3299721   0.152   0.8791  
ORdate_epoch              -0.0004865  0.0003782  -1.286   0.2000  
Hypertension.compositeyes  0.2206721  0.4335224   0.509   0.6113  
DiabetesStatusDiabetes    -0.1662155  0.3577455  -0.465   0.6428  
SmokerStatusEx-smoker      0.3280852  0.3194033   1.027   0.3057  
SmokerStatusNever smoked  -0.3228270  0.4208879  -0.767   0.4441  
Med.Statin.LLDyes         -0.1675941  0.3203904  -0.523   0.6015  
Med.all.antiplateletyes    0.6314403  0.5003615   1.262   0.2086  
GFR_MDRD                  -0.0077964  0.0076901  -1.014   0.3120  
BMI                        0.0184435  0.0358293   0.515   0.6073  
MedHx_CVDNo                0.0423863  0.2942634   0.144   0.8856  
stenose50-70%             -0.3865843  2.2624979  -0.171   0.8645  
stenose70-90%             -1.0672027  1.9694918  -0.542   0.5886  
stenose90-99%             -0.8277846  1.9615781  -0.422   0.6735  
stenose100% (Occlusion)   -1.7196836  2.4656684  -0.697   0.4864  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.902 on 184 degrees of freedom
Multiple R-squared:  0.07161,   Adjusted R-squared:  -0.01417 
F-statistic: 0.8348 on 17 and 184 DF,  p-value: 0.6515

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 0.064608 
Standard error............: 0.14602 
Odds ratio (effect size)..: 1.067 
Lower 95% CI..............: 0.801 
Upper 95% CI..............: 1.42 
T-value...................: 0.442457 
P-value...................: 0.658678 
R^2.......................: 0.071606 
Adjusted r^2..............: -0.014169 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing VEGFA_rank
filter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
                7.44057                 -0.00056                  0.82058  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4898 -1.0462 -0.3979  0.4355  8.6445 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                8.4076949  5.5460120   1.516    0.131
currentDF[, TRAIT]         0.0030833  0.1484952   0.021    0.983
Age                       -0.0114980  0.0194348  -0.592    0.555
Gendermale                 0.1454983  0.3295097   0.442    0.659
ORdate_epoch              -0.0004565  0.0004122  -1.107    0.270
Hypertension.compositeyes  0.0272018  0.4402625   0.062    0.951
DiabetesStatusDiabetes    -0.0635258  0.3535171  -0.180    0.858
SmokerStatusEx-smoker      0.4179008  0.3143016   1.330    0.186
SmokerStatusNever smoked   0.0167011  0.4304576   0.039    0.969
Med.Statin.LLDyes         -0.3020801  0.3159510  -0.956    0.340
Med.all.antiplateletyes    0.7293476  0.4719158   1.546    0.124
GFR_MDRD                  -0.0055998  0.0069856  -0.802    0.424
BMI                        0.0018191  0.0361433   0.050    0.960
MedHx_CVDNo                0.0469057  0.2934452   0.160    0.873
stenose70-90%             -1.1734158  1.3536603  -0.867    0.387
stenose90-99%             -1.1737598  1.3410416  -0.875    0.383
stenose100% (Occlusion)   -1.8834068  1.9091247  -0.987    0.325

Residual standard error: 1.765 on 160 degrees of freedom
Multiple R-squared:  0.06356,   Adjusted R-squared:  -0.03008 
F-statistic: 0.6788 on 16 and 160 DF,  p-value: 0.8119

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: VEGFA_rank 
Effect size...............: 0.003083 
Standard error............: 0.148495 
Odds ratio (effect size)..: 1.003 
Lower 95% CI..............: 0.75 
Upper 95% CI..............: 1.342 
T-value...................: 0.020764 
P-value...................: 0.9834601 
R^2.......................: 0.063562 
Adjusted r^2..............: -0.030081 
Sample size of AE DB......: 622 
Sample size of model......: 177 
Missing data %............: 71.54341 

- processing TGFB_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              8.5969382               -0.0006502                0.8662859  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5453 -1.1482 -0.3511  0.4641 10.9405 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               10.0835119  5.4966546   1.834   0.0682 .
currentDF[, TRAIT]        -0.0321781  0.1395233  -0.231   0.8179  
Age                       -0.0296836  0.0194897  -1.523   0.1295  
Gendermale                 0.0149922  0.3270053   0.046   0.9635  
ORdate_epoch              -0.0005330  0.0003651  -1.460   0.1460  
Hypertension.compositeyes  0.2074856  0.4251178   0.488   0.6261  
DiabetesStatusDiabetes    -0.2805729  0.3466341  -0.809   0.4193  
SmokerStatusEx-smoker      0.3552335  0.3184206   1.116   0.2660  
SmokerStatusNever smoked  -0.2386515  0.4223690  -0.565   0.5727  
Med.Statin.LLDyes         -0.1765074  0.3195223  -0.552   0.5813  
Med.all.antiplateletyes    0.6053299  0.5135875   1.179   0.2401  
GFR_MDRD                  -0.0069050  0.0076202  -0.906   0.3660  
BMI                        0.0167880  0.0360552   0.466   0.6420  
MedHx_CVDNo                0.0130443  0.2916376   0.045   0.9644  
stenose50-70%             -0.2923283  2.2703965  -0.129   0.8977  
stenose70-90%             -0.9506879  1.9608958  -0.485   0.6284  
stenose90-99%             -0.7416988  1.9542817  -0.380   0.7047  
stenose100% (Occlusion)   -1.6865560  2.4790511  -0.680   0.4972  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.897 on 185 degrees of freedom
Multiple R-squared:  0.06686,   Adjusted R-squared:  -0.01889 
F-statistic: 0.7797 on 17 and 185 DF,  p-value: 0.715

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: TGFB_rank 
Effect size...............: -0.032178 
Standard error............: 0.139523 
Odds ratio (effect size)..: 0.968 
Lower 95% CI..............: 0.737 
Upper 95% CI..............: 1.273 
T-value...................: -0.230629 
P-value...................: 0.8178578 
R^2.......................: 0.066858 
Adjusted r^2..............: -0.01889 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing MMP2_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              9.1158316               -0.0006947                0.8370233  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5502 -1.0774 -0.3665  0.3748 10.7233 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               11.8165182  5.6240622   2.101   0.0370 *
currentDF[, TRAIT]        -0.1455053  0.1435613  -1.014   0.3121  
Age                       -0.0325070  0.0191443  -1.698   0.0912 .
Gendermale                 0.0005085  0.3238423   0.002   0.9987  
ORdate_epoch              -0.0006510  0.0003712  -1.754   0.0812 .
Hypertension.compositeyes  0.1567524  0.4378420   0.358   0.7207  
DiabetesStatusDiabetes    -0.2355240  0.3506284  -0.672   0.5026  
SmokerStatusEx-smoker      0.3928014  0.3171961   1.238   0.2172  
SmokerStatusNever smoked  -0.1133672  0.4188201  -0.271   0.7869  
Med.Statin.LLDyes         -0.2805393  0.3150225  -0.891   0.3743  
Med.all.antiplateletyes    0.6314303  0.4946244   1.277   0.2034  
GFR_MDRD                  -0.0086571  0.0073387  -1.180   0.2397  
BMI                        0.0220154  0.0364949   0.603   0.5471  
MedHx_CVDNo               -0.0927279  0.2905263  -0.319   0.7500  
stenose50-70%             -0.2265652  2.2393825  -0.101   0.9195  
stenose70-90%             -0.9968618  1.9440252  -0.513   0.6087  
stenose90-99%             -0.7937335  1.9356648  -0.410   0.6822  
stenose100% (Occlusion)   -1.7087291  2.4420600  -0.700   0.4850  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.882 on 184 degrees of freedom
Multiple R-squared:  0.07899,   Adjusted R-squared:  -0.006099 
F-statistic: 0.9283 on 17 and 184 DF,  p-value: 0.5417

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MMP2_rank 
Effect size...............: -0.145505 
Standard error............: 0.143561 
Odds ratio (effect size)..: 0.865 
Lower 95% CI..............: 0.653 
Upper 95% CI..............: 1.146 
T-value...................: -1.013542 
P-value...................: 0.3121325 
R^2.......................: 0.078994 
Adjusted r^2..............: -0.006099 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP8_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              9.1158316               -0.0006947                0.8370233  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5848 -1.0344 -0.3781  0.4619 11.0267 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               11.3313907  5.5256894   2.051   0.0417 *
currentDF[, TRAIT]         0.1793806  0.1384327   1.296   0.1967  
Age                       -0.0289411  0.0190537  -1.519   0.1305  
Gendermale                -0.0302667  0.3254986  -0.093   0.9260  
ORdate_epoch              -0.0006022  0.0003648  -1.651   0.1005  
Hypertension.compositeyes  0.2261307  0.4314795   0.524   0.6009  
DiabetesStatusDiabetes    -0.1850047  0.3526851  -0.525   0.6005  
SmokerStatusEx-smoker      0.3918467  0.3162410   1.239   0.2169  
SmokerStatusNever smoked  -0.1668384  0.4143366  -0.403   0.6877  
Med.Statin.LLDyes         -0.2747123  0.3144564  -0.874   0.3835  
Med.all.antiplateletyes    0.6310602  0.4933463   1.279   0.2025  
GFR_MDRD                  -0.0062188  0.0073522  -0.846   0.3987  
BMI                        0.0207098  0.0364653   0.568   0.5708  
MedHx_CVDNo               -0.0470137  0.2913370  -0.161   0.8720  
stenose50-70%             -0.7238109  2.2515305  -0.321   0.7482  
stenose70-90%             -1.6070731  1.9855952  -0.809   0.4194  
stenose90-99%             -1.3344907  1.9694917  -0.678   0.4989  
stenose100% (Occlusion)   -2.3020084  2.4813884  -0.928   0.3548  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.879 on 184 degrees of freedom
Multiple R-squared:  0.08223,   Adjusted R-squared:  -0.002567 
F-statistic: 0.9697 on 17 and 184 DF,  p-value: 0.4942

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MMP8_rank 
Effect size...............: 0.179381 
Standard error............: 0.138433 
Odds ratio (effect size)..: 1.196 
Lower 95% CI..............: 0.912 
Upper 95% CI..............: 1.569 
T-value...................: 1.295796 
P-value...................: 0.1966688 
R^2.......................: 0.082227 
Adjusted r^2..............: -0.002567 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP9_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
            (Intercept)             ORdate_epoch  Med.all.antiplateletyes  
              9.1158316               -0.0006947                0.8370233  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7493 -1.0371 -0.3578  0.4994 11.0687 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                9.9635022  5.5525423   1.794   0.0744 .
currentDF[, TRAIT]         0.1471965  0.1369075   1.075   0.2837  
Age                       -0.0284371  0.0191412  -1.486   0.1391  
Gendermale                 0.0195070  0.3217730   0.061   0.9517  
ORdate_epoch              -0.0005348  0.0003677  -1.454   0.1475  
Hypertension.compositeyes  0.3075414  0.4384491   0.701   0.4839  
DiabetesStatusDiabetes    -0.1941697  0.3532322  -0.550   0.5832  
SmokerStatusEx-smoker      0.3593397  0.3162753   1.136   0.2574  
SmokerStatusNever smoked  -0.2028425  0.4160273  -0.488   0.6264  
Med.Statin.LLDyes         -0.2894530  0.3151041  -0.919   0.3595  
Med.all.antiplateletyes    0.5650494  0.4944474   1.143   0.2546  
GFR_MDRD                  -0.0062837  0.0073910  -0.850   0.3963  
BMI                        0.0228651  0.0364273   0.628   0.5310  
MedHx_CVDNo               -0.0575644  0.2913852  -0.198   0.8436  
stenose50-70%             -0.3909120  2.2359932  -0.175   0.8614  
stenose70-90%             -1.1580666  1.9447100  -0.595   0.5522  
stenose90-99%             -0.9236652  1.9361704  -0.477   0.6339  
stenose100% (Occlusion)   -1.7340090  2.4413862  -0.710   0.4784  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.881 on 184 degrees of freedom
Multiple R-squared:  0.07963,   Adjusted R-squared:  -0.0054 
F-statistic: 0.9365 on 17 and 184 DF,  p-value: 0.5322

Analyzing in dataset ' AEDB.CEA ' the association of ' PCSK9 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: PCSK9 
Trait/outcome.............: MMP9_rank 
Effect size...............: 0.147196 
Standard error............: 0.136908 
Odds ratio (effect size)..: 1.159 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.515 
T-value...................: 1.075153 
P-value...................: 0.2837141 
R^2.......................: 0.079634 
Adjusted r^2..............: -0.0054 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

Analysis of COL4A1.

- processing IL2_rank
filter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      194.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-265.51 -120.92  -58.66   43.09 1490.10 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -235.55951  799.85786  -0.295    0.769
currentDF[, TRAIT]         -27.02249   20.67127  -1.307    0.193
Age                         -1.46073    2.76107  -0.529    0.598
Gendermale                  36.57245   49.67079   0.736    0.463
ORdate_epoch                 0.04164    0.05704   0.730    0.467
Hypertension.compositeyes   30.18003   64.67084   0.467    0.641
DiabetesStatusDiabetes     -63.16913   54.18943  -1.166    0.246
SmokerStatusEx-smoker       20.26044   46.44098   0.436    0.663
SmokerStatusNever smoked    22.15109   63.23205   0.350    0.727
Med.Statin.LLDyes          -30.66003   45.62153  -0.672    0.503
Med.all.antiplateletyes    -16.20899   72.29245  -0.224    0.823
GFR_MDRD                    -0.18939    1.18639  -0.160    0.873
BMI                         -2.21988    6.15534  -0.361    0.719
MedHx_CVDNo                -15.19596   43.16217  -0.352    0.725
stenose70-90%               84.54864  184.67751   0.458    0.648
stenose90-99%               56.18741  183.49487   0.306    0.760
stenose100% (Occlusion)    165.67079  267.26390   0.620    0.536

Residual standard error: 246.3 on 146 degrees of freedom
Multiple R-squared:  0.04459,   Adjusted R-squared:  -0.06011 
F-statistic: 0.4259 on 16 and 146 DF,  p-value: 0.9737

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL2_rank 
Effect size...............: -27.02249 
Standard error............: 20.67126 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 724467.2 
T-value...................: -1.307249 
P-value...................: 0.1931832 
R^2.......................: 0.044589 
Adjusted r^2..............: -0.060114 
Sample size of AE DB......: 622 
Sample size of model......: 163 
Missing data %............: 73.79421 

- processing IL4_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + DiabetesStatus, 
    data = currentDF)

Coefficients:
           (Intercept)            ORdate_epoch  DiabetesStatusDiabetes  
            -1096.9488                  0.1068               -104.3291  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-262.90 -145.64  -70.33   30.37 1980.90 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -726.39920 1111.92746  -0.653    0.515
currentDF[, TRAIT]         -18.42443   28.70778  -0.642    0.522
Age                         -1.29553    3.76264  -0.344    0.731
Gendermale                  82.31981   67.21106   1.225    0.223
ORdate_epoch                 0.09498    0.07977   1.191    0.236
Hypertension.compositeyes   71.48231   88.28188   0.810    0.420
DiabetesStatusDiabetes    -104.18357   73.43363  -1.419    0.158
SmokerStatusEx-smoker      -31.33685   61.25995  -0.512    0.610
SmokerStatusNever smoked    -8.37049   87.04936  -0.096    0.924
Med.Statin.LLDyes          -31.28422   64.25256  -0.487    0.627
Med.all.antiplateletyes    -18.09546   96.44177  -0.188    0.851
GFR_MDRD                    -1.71086    1.75210  -0.976    0.331
BMI                         -4.99584    8.77458  -0.569    0.570
MedHx_CVDNo                 35.02269   58.46106   0.599    0.550
stenose70-90%               59.65616  237.83277   0.251    0.802
stenose90-99%               19.69623  235.93237   0.083    0.934
stenose100% (Occlusion)    117.55732  344.72696   0.341    0.734

Residual standard error: 313.4 on 128 degrees of freedom
Multiple R-squared:  0.06689,   Adjusted R-squared:  -0.04975 
F-statistic: 0.5735 on 16 and 128 DF,  p-value: 0.8988

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL4_rank 
Effect size...............: -18.42443 
Standard error............: 28.70778 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.722238e+16 
T-value...................: -0.641792 
P-value...................: 0.5221558 
R^2.......................: 0.066892 
Adjusted r^2..............: -0.049747 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing IL5_rank
filter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            207.79              -43.72  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-285.25 -135.11  -69.41   36.23 2014.53 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -519.27773 1004.69597  -0.517    0.606
currentDF[, TRAIT]         -40.85719   25.61601  -1.595    0.113
Age                         -0.80350    3.54545  -0.227    0.821
Gendermale                  74.97786   61.83281   1.213    0.227
ORdate_epoch                 0.06409    0.07329   0.874    0.383
Hypertension.compositeyes   46.59942   81.93368   0.569    0.570
DiabetesStatusDiabetes     -81.83172   66.60199  -1.229    0.221
SmokerStatusEx-smoker      -39.20558   56.32640  -0.696    0.488
SmokerStatusNever smoked   -29.29980   81.34543  -0.360    0.719
Med.Statin.LLDyes           -8.64441   57.94846  -0.149    0.882
Med.all.antiplateletyes     -3.47943   89.76312  -0.039    0.969
GFR_MDRD                    -0.67672    1.53928  -0.440    0.661
BMI                         -4.44524    7.64153  -0.582    0.562
MedHx_CVDNo                 37.77590   54.65942   0.691    0.491
stenose70-90%              104.07280  189.19251   0.550    0.583
stenose90-99%               78.14027  186.88246   0.418    0.676
stenose100% (Occlusion)    151.12428  302.03115   0.500    0.618

Residual standard error: 302.6 on 141 degrees of freedom
Multiple R-squared:  0.06409,   Adjusted R-squared:  -0.04211 
F-statistic: 0.6035 on 16 and 141 DF,  p-value: 0.8771

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL5_rank 
Effect size...............: -40.85719 
Standard error............: 25.61601 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 11500.87 
T-value...................: -1.594987 
P-value...................: 0.1129542 
R^2.......................: 0.064091 
Adjusted r^2..............: -0.042111 
Sample size of AE DB......: 622 
Sample size of model......: 158 
Missing data %............: 74.59807 

- processing IL6_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      201.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-244.09 -128.23  -71.25   39.17 2056.11 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -672.79596 1009.07831  -0.667    0.506
currentDF[, TRAIT]         -23.42988   24.18245  -0.969    0.334
Age                         -0.61030    3.53466  -0.173    0.863
Gendermale                  59.50579   58.17290   1.023    0.308
ORdate_epoch                 0.06128    0.06723   0.912    0.363
Hypertension.compositeyes   36.28956   77.72547   0.467    0.641
DiabetesStatusDiabetes     -87.18491   65.03275  -1.341    0.182
SmokerStatusEx-smoker      -20.05057   55.79281  -0.359    0.720
SmokerStatusNever smoked   -25.49971   77.40832  -0.329    0.742
Med.Statin.LLDyes           -0.87523   54.99147  -0.016    0.987
Med.all.antiplateletyes    -21.54612   89.17710  -0.242    0.809
GFR_MDRD                    -1.00792    1.50298  -0.671    0.504
BMI                         -0.34297    6.44664  -0.053    0.958
MedHx_CVDNo                 35.63352   52.43195   0.680    0.498
stenose50-70%              114.44884  358.71015   0.319    0.750
stenose70-90%              199.63081  311.65065   0.641    0.523
stenose90-99%              187.36981  311.07946   0.602    0.548
stenose100% (Occlusion)    307.59523  392.68776   0.783    0.435

Residual standard error: 299.1 on 146 degrees of freedom
Multiple R-squared:  0.04357,   Adjusted R-squared:  -0.06779 
F-statistic: 0.3913 on 17 and 146 DF,  p-value: 0.9856

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL6_rank 
Effect size...............: -23.42988 
Standard error............: 24.18245 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 25647775143 
T-value...................: -0.968879 
P-value...................: 0.3342076 
R^2.......................: 0.043572 
Adjusted r^2..............: -0.067793 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL8_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      193.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-265.59 -114.58  -50.70   43.23 2016.10 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -518.81005  951.94117  -0.545   0.5866  
currentDF[, TRAIT]          12.54758   23.00569   0.545   0.5864  
Age                         -2.45231    3.11351  -0.788   0.4323  
Gendermale                  36.36685   55.59383   0.654   0.5141  
ORdate_epoch                 0.06663    0.06336   1.052   0.2949  
Hypertension.compositeyes    7.33503   68.96008   0.106   0.9154  
DiabetesStatusDiabetes     -84.33342   58.41672  -1.444   0.1511  
SmokerStatusEx-smoker      -15.71706   52.19125  -0.301   0.7638  
SmokerStatusNever smoked   -46.22330   75.11322  -0.615   0.5393  
Med.Statin.LLDyes          -12.43799   49.98050  -0.249   0.8038  
Med.all.antiplateletyes     51.04631   77.00414   0.663   0.5085  
GFR_MDRD                    -2.31524    1.23154  -1.880   0.0623 .
BMI                         -1.19257    5.88440  -0.203   0.8397  
MedHx_CVDNo                 39.84967   48.71897   0.818   0.4148  
stenose50-70%              137.96083  319.04121   0.432   0.6661  
stenose70-90%              167.07095  278.62857   0.600   0.5498  
stenose90-99%              193.44046  277.61510   0.697   0.4871  
stenose100% (Occlusion)    581.55968  396.34312   1.467   0.1446  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 266.3 on 136 degrees of freedom
Multiple R-squared:  0.07732,   Adjusted R-squared:  -0.03801 
F-statistic: 0.6704 on 17 and 136 DF,  p-value: 0.8273

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL8_rank 
Effect size...............: 12.54758 
Standard error............: 23.00569 
Odds ratio (effect size)..: 281412.6 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.076922e+25 
T-value...................: 0.545412 
P-value...................: 0.5863637 
R^2.......................: 0.077323 
Adjusted r^2..............: -0.038012 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing IL9_rank
filter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            208.69               42.63  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-283.67 -135.59  -56.86   16.93 1848.93 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.085e+03  9.061e+02  -1.198   0.2326  
currentDF[, TRAIT]         4.897e+01  2.282e+01   2.146   0.0333 *
Age                        2.438e+00  3.321e+00   0.734   0.4638  
Gendermale                 7.297e+01  5.505e+01   1.326   0.1868  
ORdate_epoch               6.840e-02  5.936e-02   1.152   0.2509  
Hypertension.compositeyes  1.034e+02  7.433e+01   1.392   0.1659  
DiabetesStatusDiabetes    -8.556e+01  6.152e+01  -1.391   0.1662  
SmokerStatusEx-smoker     -8.302e+01  5.378e+01  -1.544   0.1245  
SmokerStatusNever smoked  -1.026e+02  6.848e+01  -1.498   0.1359  
Med.Statin.LLDyes          3.291e+01  5.438e+01   0.605   0.5459  
Med.all.antiplateletyes   -1.454e+01  8.990e+01  -0.162   0.8717  
GFR_MDRD                  -5.388e-01  1.251e+00  -0.431   0.6673  
BMI                        1.545e-01  6.057e+00   0.026   0.9797  
MedHx_CVDNo                7.149e+01  4.970e+01   1.438   0.1522  
stenose50-70%              3.519e+01  3.869e+02   0.091   0.9276  
stenose70-90%              2.081e+02  3.135e+02   0.664   0.5077  
stenose90-99%              1.824e+02  3.122e+02   0.584   0.5598  
stenose100% (Occlusion)    3.927e+02  3.971e+02   0.989   0.3241  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 303.2 on 168 degrees of freedom
Multiple R-squared:  0.08982,   Adjusted R-squared:  -0.00228 
F-statistic: 0.9752 on 17 and 168 DF,  p-value: 0.4883

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL9_rank 
Effect size...............: 48.97253 
Standard error............: 22.81682 
Odds ratio (effect size)..: 1.855664e+21 
Lower 95% CI..............: 70.216 
Upper 95% CI..............: 4.904165e+40 
T-value...................: 2.146335 
P-value...................: 0.03328133 
R^2.......................: 0.089822 
Adjusted r^2..............: -0.00228 
Sample size of AE DB......: 622 
Sample size of model......: 186 
Missing data %............: 70.09646 

- processing IL10_rank
filter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes  
                 206.9                   -87.0  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-285.03 -123.18  -57.37   43.28 1518.05 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -499.23251  994.49557  -0.502    0.617
currentDF[, TRAIT]         -14.69025   24.14643  -0.608    0.544
Age                         -1.14538    3.24460  -0.353    0.725
Gendermale                  48.25094   57.18363   0.844    0.400
ORdate_epoch                 0.05306    0.06959   0.762    0.447
Hypertension.compositeyes   36.62935   75.60343   0.484    0.629
DiabetesStatusDiabetes     -85.33815   62.45513  -1.366    0.174
SmokerStatusEx-smoker       14.25301   52.78951   0.270    0.788
SmokerStatusNever smoked    39.36742   73.46513   0.536    0.593
Med.Statin.LLDyes          -35.22363   52.64662  -0.669    0.505
Med.all.antiplateletyes    -13.39168   81.84390  -0.164    0.870
GFR_MDRD                    -0.08557    1.50052  -0.057    0.955
BMI                          0.60656    7.41275   0.082    0.935
MedHx_CVDNo                -13.96550   51.78926  -0.270    0.788
stenose70-90%              103.94912  199.75411   0.520    0.604
stenose90-99%               56.99770  198.15900   0.288    0.774
stenose100% (Occlusion)    180.87809  289.83695   0.624    0.534

Residual standard error: 262.4 on 122 degrees of freedom
Multiple R-squared:  0.05324,   Adjusted R-squared:  -0.07092 
F-statistic: 0.4288 on 16 and 122 DF,  p-value: 0.9722

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL10_rank 
Effect size...............: -14.69025 
Standard error............: 24.14644 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.492683e+14 
T-value...................: -0.608382 
P-value...................: 0.5440655 
R^2.......................: 0.053244 
Adjusted r^2..............: -0.070921 
Sample size of AE DB......: 622 
Sample size of model......: 139 
Missing data %............: 77.65273 

- processing IL12_rank
filter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            214.39              -44.02  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-292.48 -142.60  -72.27   39.30 1988.42 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -381.27697 1157.08904  -0.330    0.742
currentDF[, TRAIT]         -37.66844   28.14940  -1.338    0.183
Age                         -0.94776    3.88336  -0.244    0.808
Gendermale                  81.20956   66.57541   1.220    0.225
ORdate_epoch                 0.06289    0.08057   0.781    0.436
Hypertension.compositeyes   41.87433   90.54924   0.462    0.645
DiabetesStatusDiabetes     -90.08678   72.14080  -1.249    0.214
SmokerStatusEx-smoker      -41.31605   64.02289  -0.645    0.520
SmokerStatusNever smoked   -30.93369   84.95212  -0.364    0.716
Med.Statin.LLDyes          -30.60346   63.72495  -0.480    0.632
Med.all.antiplateletyes    -14.26043   96.85256  -0.147    0.883
GFR_MDRD                    -0.83930    1.71410  -0.490    0.625
BMI                         -5.47018    8.67344  -0.631    0.529
MedHx_CVDNo                 11.77589   58.42861   0.202    0.841
stenose70-90%               77.07031  237.16313   0.325    0.746
stenose90-99%               39.23630  236.21559   0.166    0.868
stenose100% (Occlusion)    334.04755  415.54790   0.804    0.423

Residual standard error: 313.2 on 129 degrees of freedom
Multiple R-squared:  0.07145,   Adjusted R-squared:  -0.04372 
F-statistic: 0.6204 on 16 and 129 DF,  p-value: 0.8628

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL12_rank 
Effect size...............: -37.66844 
Standard error............: 28.1494 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 39999256 
T-value...................: -1.338162 
P-value...................: 0.1831977 
R^2.......................: 0.071452 
Adjusted r^2..............: -0.043717 
Sample size of AE DB......: 622 
Sample size of model......: 146 
Missing data %............: 76.52733 

- processing IL13_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + MedHx_CVD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]         MedHx_CVDNo  
            186.20               36.92               63.18  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-295.01 -144.00  -65.03   14.12 1877.75 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.039e+03  8.828e+02  -1.177   0.2408  
currentDF[, TRAIT]         4.708e+01  2.274e+01   2.070   0.0398 *
Age                        4.121e+00  3.140e+00   1.312   0.1910  
Gendermale                 6.160e+01  5.266e+01   1.170   0.2436  
ORdate_epoch               6.249e-02  5.857e-02   1.067   0.2874  
Hypertension.compositeyes  5.572e+01  6.876e+01   0.810   0.4188  
DiabetesStatusDiabetes    -7.413e+01  5.693e+01  -1.302   0.1945  
SmokerStatusEx-smoker     -9.553e+01  5.136e+01  -1.860   0.0645 .
SmokerStatusNever smoked  -1.153e+02  6.722e+01  -1.715   0.0880 .
Med.Statin.LLDyes          1.682e+01  5.106e+01   0.329   0.7423  
Med.all.antiplateletyes   -4.355e+01  7.951e+01  -0.548   0.5846  
GFR_MDRD                  -4.827e-01  1.217e+00  -0.397   0.6922  
BMI                        1.811e+00  5.698e+00   0.318   0.7510  
MedHx_CVDNo                6.948e+01  4.655e+01   1.493   0.1372  
stenose50-70%              5.818e+01  3.608e+02   0.161   0.8721  
stenose70-90%              1.760e+02  3.124e+02   0.563   0.5738  
stenose90-99%              1.401e+02  3.120e+02   0.449   0.6539  
stenose100% (Occlusion)    3.305e+02  3.912e+02   0.845   0.3993  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 302.2 on 184 degrees of freedom
Multiple R-squared:  0.07791,   Adjusted R-squared:  -0.007279 
F-statistic: 0.9146 on 17 and 184 DF,  p-value: 0.5578

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL13_rank 
Effect size...............: 47.08246 
Standard error............: 22.74296 
Odds ratio (effect size)..: 2.803189e+20 
Lower 95% CI..............: 12.259 
Upper 95% CI..............: 6.409918e+39 
T-value...................: 2.070199 
P-value...................: 0.03983027 
R^2.......................: 0.077913 
Adjusted r^2..............: -0.007279 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing IL21_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + MedHx_CVD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]         MedHx_CVDNo  
            186.72               30.23               62.98  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-284.51 -145.05  -69.31   16.11 1905.00 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.058e+03  8.871e+02  -1.193   0.2344  
currentDF[, TRAIT]         3.653e+01  2.267e+01   1.612   0.1088  
Age                        3.581e+00  3.126e+00   1.146   0.2535  
Gendermale                 6.246e+01  5.299e+01   1.179   0.2400  
ORdate_epoch               6.720e-02  5.872e-02   1.144   0.2540  
Hypertension.compositeyes  4.883e+01  6.890e+01   0.709   0.4794  
DiabetesStatusDiabetes    -7.807e+01  5.710e+01  -1.367   0.1733  
SmokerStatusEx-smoker     -8.820e+01  5.128e+01  -1.720   0.0871 .
SmokerStatusNever smoked  -1.067e+02  6.723e+01  -1.587   0.1141  
Med.Statin.LLDyes          1.717e+01  5.137e+01   0.334   0.7386  
Med.all.antiplateletyes   -4.414e+01  7.992e+01  -0.552   0.5814  
GFR_MDRD                  -5.713e-01  1.222e+00  -0.468   0.6406  
BMI                        1.463e+00  5.718e+00   0.256   0.7984  
MedHx_CVDNo                6.735e+01  4.673e+01   1.441   0.1512  
stenose50-70%              8.149e+01  3.620e+02   0.225   0.8222  
stenose70-90%              1.897e+02  3.137e+02   0.605   0.5460  
stenose90-99%              1.571e+02  3.133e+02   0.501   0.6167  
stenose100% (Occlusion)    3.179e+02  3.930e+02   0.809   0.4196  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 303.6 on 184 degrees of freedom
Multiple R-squared:  0.06957,   Adjusted R-squared:  -0.01639 
F-statistic: 0.8093 on 17 and 184 DF,  p-value: 0.6811

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IL21_rank 
Effect size...............: 36.52902 
Standard error............: 22.66624 
Odds ratio (effect size)..: 7.31729e+15 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.439608e+35 
T-value...................: 1.611605 
P-value...................: 0.108762 
R^2.......................: 0.06957 
Adjusted r^2..............: -0.016394 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing INFG_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + DiabetesStatus, 
    data = currentDF)

Coefficients:
           (Intercept)            ORdate_epoch  DiabetesStatusDiabetes  
            -943.36164                 0.09447               -96.03819  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-247.90 -134.16  -71.69   21.14 2005.78 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.031e+03  1.109e+03  -0.930    0.354
currentDF[, TRAIT]         3.189e+00  2.872e+01   0.111    0.912
Age                        3.968e-01  3.657e+00   0.109    0.914
Gendermale                 7.930e+01  6.674e+01   1.188    0.237
ORdate_epoch               8.354e-02  7.505e-02   1.113    0.268
Hypertension.compositeyes  2.570e+01  8.840e+01   0.291    0.772
DiabetesStatusDiabetes    -9.669e+01  6.713e+01  -1.440    0.152
SmokerStatusEx-smoker     -2.931e+01  6.046e+01  -0.485    0.629
SmokerStatusNever smoked  -1.262e+01  8.219e+01  -0.154    0.878
Med.Statin.LLDyes         -4.047e+00  5.976e+01  -0.068    0.946
Med.all.antiplateletyes    1.969e+01  8.760e+01   0.225    0.822
GFR_MDRD                  -1.159e+00  1.477e+00  -0.785    0.434
BMI                       -8.356e-01  6.826e+00  -0.122    0.903
MedHx_CVDNo                4.045e+01  5.762e+01   0.702    0.484
stenose50-70%              1.264e+02  3.699e+02   0.342    0.733
stenose70-90%              2.202e+02  3.227e+02   0.682    0.496
stenose90-99%              1.955e+02  3.209e+02   0.609    0.543
stenose100% (Occlusion)    5.515e+02  4.561e+02   1.209    0.229

Residual standard error: 307.9 on 136 degrees of freedom
Multiple R-squared:  0.05709,   Adjusted R-squared:  -0.06077 
F-statistic: 0.4844 on 17 and 136 DF,  p-value: 0.9565

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: INFG_rank 
Effect size...............: 3.188903 
Standard error............: 28.72333 
Odds ratio (effect size)..: 24.262 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.834599e+25 
T-value...................: 0.111021 
P-value...................: 0.9117631 
R^2.......................: 0.057094 
Adjusted r^2..............: -0.06077 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing TNFA_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus, 
    data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]  DiabetesStatusDiabetes  
                227.08                  -40.58                 -101.18  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-295.56 -148.59  -68.94   45.75 1986.78 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -199.43719 1113.16229  -0.179    0.858
currentDF[, TRAIT]         -40.28898   27.99360  -1.439    0.153
Age                         -1.04813    3.78162  -0.277    0.782
Gendermale                  70.91567   66.05648   1.074    0.285
ORdate_epoch                 0.05271    0.07757   0.679    0.498
Hypertension.compositeyes   51.73804   88.53143   0.584    0.560
DiabetesStatusDiabetes    -103.90204   71.48569  -1.453    0.149
SmokerStatusEx-smoker       -8.23006   61.63023  -0.134    0.894
SmokerStatusNever smoked   -19.36882   85.36463  -0.227    0.821
Med.Statin.LLDyes          -40.93955   63.49470  -0.645    0.520
Med.all.antiplateletyes    -21.38365   95.29580  -0.224    0.823
GFR_MDRD                    -0.94346    1.64202  -0.575    0.567
BMI                         -7.07033    8.13204  -0.869    0.386
MedHx_CVDNo                 17.60246   59.76589   0.295    0.769
stenose70-90%               71.88210  236.10051   0.304    0.761
stenose90-99%               33.62977  233.70260   0.144    0.886
stenose100% (Occlusion)    334.04664  411.78015   0.811    0.419

Residual standard error: 312.1 on 128 degrees of freedom
Multiple R-squared:  0.07392,   Adjusted R-squared:  -0.04184 
F-statistic: 0.6385 on 16 and 128 DF,  p-value: 0.8473

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: TNFA_rank 
Effect size...............: -40.28898 
Standard error............: 27.9936 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2144609 
T-value...................: -1.439221 
P-value...................: 0.1525288 
R^2.......................: 0.073918 
Adjusted r^2..............: -0.041842 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing MIF_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      211.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-266.04 -132.78  -70.50   36.84 1998.92 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.352e+03  9.657e+02  -1.401    0.163
currentDF[, TRAIT]         1.374e+01  2.644e+01   0.520    0.604
Age                        2.915e+00  3.116e+00   0.935    0.351
Gendermale                 7.091e+01  5.304e+01   1.337    0.183
ORdate_epoch               9.078e-02  6.608e-02   1.374    0.171
Hypertension.compositeyes  4.783e+01  6.973e+01   0.686    0.494
DiabetesStatusDiabetes    -8.292e+01  5.743e+01  -1.444    0.150
SmokerStatusEx-smoker     -7.682e+01  5.140e+01  -1.495    0.137
SmokerStatusNever smoked  -9.089e+01  6.690e+01  -1.359    0.176
Med.Statin.LLDyes          2.382e+01  5.152e+01   0.462    0.644
Med.all.antiplateletyes   -4.137e+01  8.062e+01  -0.513    0.608
GFR_MDRD                  -4.703e-01  1.248e+00  -0.377    0.707
BMI                        1.177e+00  5.756e+00   0.205    0.838
MedHx_CVDNo                6.506e+01  4.705e+01   1.383    0.168
stenose50-70%              1.161e+02  3.640e+02   0.319    0.750
stenose70-90%              2.084e+02  3.161e+02   0.659    0.511
stenose90-99%              1.884e+02  3.155e+02   0.597    0.551
stenose100% (Occlusion)    3.193e+02  3.959e+02   0.807    0.421

Residual standard error: 305.5 on 184 degrees of freedom
Multiple R-squared:  0.05782,   Adjusted R-squared:  -0.02923 
F-statistic: 0.6642 on 17 and 184 DF,  p-value: 0.8351

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MIF_rank 
Effect size...............: 13.74005 
Standard error............: 26.43704 
Odds ratio (effect size)..: 927318.9 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.957279e+28 
T-value...................: 0.519727 
P-value...................: 0.6038783 
R^2.......................: 0.057819 
Adjusted r^2..............: -0.02923 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MCP1_rank
filter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      212.4  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-276.84 -141.92  -66.45   24.34 1970.59 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.311e+03  9.187e+02  -1.427    0.155
currentDF[, TRAIT]         1.794e+01  2.261e+01   0.794    0.429
Age                        3.434e+00  3.157e+00   1.088    0.278
Gendermale                 6.427e+01  5.383e+01   1.194    0.234
ORdate_epoch               8.559e-02  6.079e-02   1.408    0.161
Hypertension.compositeyes  5.334e+01  7.000e+01   0.762    0.447
DiabetesStatusDiabetes    -8.357e+01  5.775e+01  -1.447    0.150
SmokerStatusEx-smoker     -8.098e+01  5.154e+01  -1.571    0.118
SmokerStatusNever smoked  -9.868e+01  6.750e+01  -1.462    0.146
Med.Statin.LLDyes          2.631e+01  5.188e+01   0.507    0.613
Med.all.antiplateletyes   -7.023e+01  8.548e+01  -0.822    0.412
GFR_MDRD                  -5.591e-01  1.238e+00  -0.452    0.652
BMI                        1.755e+00  5.859e+00   0.300    0.765
MedHx_CVDNo                6.498e+01  4.740e+01   1.371    0.172
stenose50-70%              1.278e+02  3.638e+02   0.351    0.726
stenose70-90%              2.154e+02  3.156e+02   0.683    0.496
stenose90-99%              1.995e+02  3.145e+02   0.634    0.527
stenose100% (Occlusion)    3.147e+02  3.974e+02   0.792    0.429

Residual standard error: 306 on 182 degrees of freedom
Multiple R-squared:  0.0625,    Adjusted R-squared:  -0.02506 
F-statistic: 0.7138 on 17 and 182 DF,  p-value: 0.7864

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MCP1_rank 
Effect size...............: 17.94467 
Standard error............: 22.6138 
Odds ratio (effect size)..: 62125723 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.10287e+27 
T-value...................: 0.793528 
P-value...................: 0.4285039 
R^2.......................: 0.062504 
Adjusted r^2..............: -0.025064 
Sample size of AE DB......: 622 
Sample size of model......: 200 
Missing data %............: 67.84566 

- processing MIP1a_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes  
                225.83                  -83.25  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-272.22 -140.34  -60.77   17.80 1926.00 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -936.58610  907.69653  -1.032    0.304
currentDF[, TRAIT]          25.72336   23.30393   1.104    0.271
Age                          2.10330    3.23022   0.651    0.516
Gendermale                  77.56203   55.81091   1.390    0.166
ORdate_epoch                 0.05939    0.05990   0.991    0.323
Hypertension.compositeyes   78.53052   73.10998   1.074    0.284
DiabetesStatusDiabetes     -93.06918   60.27219  -1.544    0.124
SmokerStatusEx-smoker      -71.66308   54.01266  -1.327    0.186
SmokerStatusNever smoked   -94.03790   69.39987  -1.355    0.177
Med.Statin.LLDyes           26.89578   53.91301   0.499    0.619
Med.all.antiplateletyes    -15.77055   90.50121  -0.174    0.862
GFR_MDRD                    -0.40702    1.26013  -0.323    0.747
BMI                          0.81558    5.96740   0.137    0.891
MedHx_CVDNo                 67.25134   50.04354   1.344    0.181
stenose50-70%               45.62049  392.47298   0.116    0.908
stenose70-90%              193.55681  317.80999   0.609    0.543
stenose90-99%              163.34929  317.29298   0.515    0.607
stenose100% (Occlusion)    321.11271  400.16640   0.802    0.423

Residual standard error: 306.4 on 171 degrees of freedom
Multiple R-squared:  0.06715,   Adjusted R-squared:  -0.02558 
F-statistic: 0.7241 on 17 and 171 DF,  p-value: 0.7753

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 25.72336 
Standard error............: 23.30393 
Odds ratio (effect size)..: 1.48427e+11 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.019113e+31 
T-value...................: 1.10382 
P-value...................: 0.2712219 
R^2.......................: 0.067154 
Adjusted r^2..............: -0.025585 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing RANTES_rank
filter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch + 
    MedHx_CVD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch         MedHx_CVDNo  
        -960.42384            38.65072             0.09144            66.29043  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-281.07 -141.74  -75.78   29.66 1922.73 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.596e+03  9.539e+02  -1.673   0.0961 .
currentDF[, TRAIT]         3.793e+01  2.503e+01   1.515   0.1314  
Age                        3.733e+00  3.186e+00   1.172   0.2428  
Gendermale                 6.345e+01  5.450e+01   1.164   0.2459  
ORdate_epoch               1.080e-01  6.431e-02   1.680   0.0947 .
Hypertension.compositeyes  6.112e+01  7.228e+01   0.846   0.3989  
DiabetesStatusDiabetes    -6.823e+01  5.893e+01  -1.158   0.2484  
SmokerStatusEx-smoker     -7.752e+01  5.258e+01  -1.475   0.1421  
SmokerStatusNever smoked  -1.044e+02  6.799e+01  -1.535   0.1265  
Med.Statin.LLDyes          2.617e+01  5.216e+01   0.502   0.6164  
Med.all.antiplateletyes   -5.094e+01  8.509e+01  -0.599   0.5501  
GFR_MDRD                  -3.899e-01  1.243e+00  -0.314   0.7542  
BMI                        5.503e-01  5.873e+00   0.094   0.9255  
MedHx_CVDNo                8.188e+01  4.877e+01   1.679   0.0949 .
stenose50-70%              8.018e+01  3.892e+02   0.206   0.8370  
stenose70-90%              1.848e+02  3.175e+02   0.582   0.5612  
stenose90-99%              1.677e+02  3.159e+02   0.531   0.5962  
stenose100% (Occlusion)    2.685e+02  4.004e+02   0.671   0.5033  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 306.5 on 180 degrees of freedom
Multiple R-squared:  0.07093,   Adjusted R-squared:  -0.01682 
F-statistic: 0.8083 on 17 and 180 DF,  p-value: 0.6822

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: RANTES_rank 
Effect size...............: 37.92743 
Standard error............: 25.02876 
Odds ratio (effect size)..: 2.962601e+16 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.978322e+37 
T-value...................: 1.515354 
P-value...................: 0.1314368 
R^2.......................: 0.070927 
Adjusted r^2..............: -0.016818 
Sample size of AE DB......: 622 
Sample size of model......: 198 
Missing data %............: 68.1672 

- processing MIG_rank
filter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            208.95               40.45  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-288.15 -143.42  -62.18   13.04 1880.67 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -688.41763  924.44822  -0.745   0.4574  
currentDF[, TRAIT]          48.01073   24.86562   1.931   0.0551 .
Age                          3.82958    3.16957   1.208   0.2285  
Gendermale                  58.53386   53.79349   1.088   0.2780  
ORdate_epoch                 0.03720    0.06218   0.598   0.5504  
Hypertension.compositeyes   52.24472   71.31848   0.733   0.4648  
DiabetesStatusDiabetes     -77.51555   58.34014  -1.329   0.1856  
SmokerStatusEx-smoker      -94.65833   52.18773  -1.814   0.0714 .
SmokerStatusNever smoked  -114.48722   68.28996  -1.676   0.0954 .
Med.Statin.LLDyes           17.85542   52.19582   0.342   0.7327  
Med.all.antiplateletyes    -43.24001   82.04131  -0.527   0.5988  
GFR_MDRD                    -0.71943    1.23264  -0.584   0.5602  
BMI                          1.50435    5.75657   0.261   0.7941  
MedHx_CVDNo                 68.36653   47.77877   1.431   0.1542  
stenose50-70%               36.18597  389.68007   0.093   0.9261  
stenose70-90%              190.12868  315.17655   0.603   0.5471  
stenose90-99%              156.73558  314.45575   0.498   0.6188  
stenose100% (Occlusion)    311.51096  395.99180   0.787   0.4325  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 304.9 on 181 degrees of freedom
Multiple R-squared:  0.07565,   Adjusted R-squared:  -0.01116 
F-statistic: 0.8714 on 17 and 181 DF,  p-value: 0.6084

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MIG_rank 
Effect size...............: 48.01073 
Standard error............: 24.86562 
Odds ratio (effect size)..: 7.092462e+20 
Lower 95% CI..............: 0.484 
Upper 95% CI..............: 1.039533e+42 
T-value...................: 1.930808 
P-value...................: 0.05506939 
R^2.......................: 0.075654 
Adjusted r^2..............: -0.011163 
Sample size of AE DB......: 622 
Sample size of model......: 199 
Missing data %............: 68.00643 

- processing IP10_rank
filter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    ORdate_epoch + DiabetesStatus, data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]              Gendermale            ORdate_epoch  DiabetesStatusDiabetes  
            -836.52363                45.79817                76.10814                 0.08007               -95.08837  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-289.11 -136.20  -57.71   35.76 1780.52 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.196e+03  9.150e+02  -1.307   0.1931  
currentDF[, TRAIT]         6.155e+01  2.450e+01   2.513   0.0129 *
Age                        3.177e+00  3.343e+00   0.950   0.3433  
Gendermale                 9.247e+01  5.488e+01   1.685   0.0939 .
ORdate_epoch               7.435e-02  6.019e-02   1.235   0.2185  
Hypertension.compositeyes  8.933e+01  7.430e+01   1.202   0.2310  
DiabetesStatusDiabetes    -8.887e+01  6.225e+01  -1.428   0.1553  
SmokerStatusEx-smoker     -1.030e+02  5.539e+01  -1.859   0.0649 .
SmokerStatusNever smoked  -1.124e+02  7.135e+01  -1.575   0.1172  
Med.Statin.LLDyes          2.775e+01  5.493e+01   0.505   0.6141  
Med.all.antiplateletyes   -2.485e+00  8.507e+01  -0.029   0.9767  
GFR_MDRD                  -6.952e-01  1.306e+00  -0.532   0.5953  
BMI                        1.022e+00  5.915e+00   0.173   0.8631  
MedHx_CVDNo                5.210e+01  5.028e+01   1.036   0.3016  
stenose50-70%              1.767e+01  3.912e+02   0.045   0.9640  
stenose70-90%              1.894e+02  3.174e+02   0.597   0.5516  
stenose90-99%              1.649e+02  3.156e+02   0.523   0.6019  
stenose100% (Occlusion)    4.253e+02  3.987e+02   1.067   0.2876  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 305.9 on 165 degrees of freedom
Multiple R-squared:  0.101, Adjusted R-squared:  0.008393 
F-statistic: 1.091 on 17 and 165 DF,  p-value: 0.3668

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: IP10_rank 
Effect size...............: 61.55343 
Standard error............: 24.49587 
Odds ratio (effect size)..: 5.399024e+26 
Lower 95% CI..............: 760347.2 
Upper 95% CI..............: 3.833705e+47 
T-value...................: 2.512809 
P-value...................: 0.01293704 
R^2.......................: 0.101016 
Adjusted r^2..............: 0.008393 
Sample size of AE DB......: 622 
Sample size of model......: 183 
Missing data %............: 70.57878 

- processing Eotaxin1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      211.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-262.23 -137.50  -72.50   22.49 1952.25 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.045e+03  9.017e+02  -1.158    0.248
currentDF[, TRAIT]         1.796e+01  2.312e+01   0.777    0.438
Age                        3.115e+00  3.132e+00   0.994    0.321
Gendermale                 6.705e+01  5.335e+01   1.257    0.210
ORdate_epoch               6.631e-02  5.992e-02   1.107    0.270
Hypertension.compositeyes  4.341e+01  6.919e+01   0.627    0.531
DiabetesStatusDiabetes    -8.152e+01  5.742e+01  -1.420    0.157
SmokerStatusEx-smoker     -8.283e+01  5.149e+01  -1.609    0.109
SmokerStatusNever smoked  -9.654e+01  6.753e+01  -1.430    0.155
Med.Statin.LLDyes          2.272e+01  5.150e+01   0.441    0.660
Med.all.antiplateletyes   -4.150e+01  8.039e+01  -0.516    0.606
GFR_MDRD                  -5.585e-01  1.229e+00  -0.455    0.650
BMI                        1.437e+00  5.752e+00   0.250    0.803
MedHx_CVDNo                6.581e+01  4.701e+01   1.400    0.163
stenose50-70%              1.109e+02  3.636e+02   0.305    0.761
stenose70-90%              2.065e+02  3.154e+02   0.655    0.513
stenose90-99%              1.815e+02  3.150e+02   0.576    0.565
stenose100% (Occlusion)    3.150e+02  3.955e+02   0.797    0.427

Residual standard error: 305.2 on 184 degrees of freedom
Multiple R-squared:  0.05952,   Adjusted R-squared:  -0.02737 
F-statistic: 0.685 on 17 and 184 DF,  p-value: 0.8153

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 17.96428 
Standard error............: 23.1227 
Odds ratio (effect size)..: 63355847 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.049465e+27 
T-value...................: 0.776911 
P-value...................: 0.4382089 
R^2.......................: 0.059521 
Adjusted r^2..............: -0.027371 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing TARC_rank
filter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + MedHx_CVD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]         MedHx_CVDNo  
            195.56               35.20               71.33  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-312.94 -149.97  -70.46   32.93 1838.11 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.739e+03  1.119e+03  -1.553   0.1223  
currentDF[, TRAIT]         5.468e+01  2.680e+01   2.040   0.0430 *
Age                        5.479e+00  3.545e+00   1.545   0.1242  
Gendermale                 9.475e+01  5.975e+01   1.586   0.1148  
ORdate_epoch               1.034e-01  7.765e-02   1.332   0.1848  
Hypertension.compositeyes  6.470e+01  7.532e+01   0.859   0.3917  
DiabetesStatusDiabetes    -8.152e+01  6.305e+01  -1.293   0.1979  
SmokerStatusEx-smoker     -8.915e+01  5.721e+01  -1.558   0.1211  
SmokerStatusNever smoked  -1.261e+02  7.309e+01  -1.726   0.0863 .
Med.Statin.LLDyes          9.712e+00  5.949e+01   0.163   0.8705  
Med.all.antiplateletyes   -3.634e+01  9.220e+01  -0.394   0.6940  
GFR_MDRD                  -6.769e-02  1.381e+00  -0.049   0.9610  
BMI                        1.143e+00  6.375e+00   0.179   0.8579  
MedHx_CVDNo                7.814e+01  5.250e+01   1.488   0.1387  
stenose50-70%              1.671e+02  4.031e+02   0.415   0.6790  
stenose70-90%              2.413e+02  3.294e+02   0.733   0.4648  
stenose90-99%              1.848e+02  3.283e+02   0.563   0.5743  
stenose100% (Occlusion)    3.656e+02  4.180e+02   0.874   0.3832  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 318.4 on 160 degrees of freedom
Multiple R-squared:  0.08749,   Adjusted R-squared:  -0.009463 
F-statistic: 0.9024 on 17 and 160 DF,  p-value: 0.5722

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: TARC_rank 
Effect size...............: 54.67634 
Standard error............: 26.80373 
Odds ratio (effect size)..: 5.567137e+23 
Lower 95% CI..............: 8.508 
Upper 95% CI..............: 3.642712e+46 
T-value...................: 2.039878 
P-value...................: 0.04300705 
R^2.......................: 0.087491 
Adjusted r^2..............: -0.009463 
Sample size of AE DB......: 622 
Sample size of model......: 178 
Missing data %............: 71.38264 

- processing PARC_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -1071.1425             42.2875              0.1021  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-281.93 -144.01  -67.22   37.34 1908.50 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.882e+03  9.532e+02  -1.974   0.0499 *
currentDF[, TRAIT]         4.843e+01  2.425e+01   1.997   0.0473 *
Age                        3.338e+00  3.087e+00   1.081   0.2811  
Gendermale                 7.572e+01  5.248e+01   1.443   0.1507  
ORdate_epoch               1.243e-01  6.326e-02   1.964   0.0510 .
Hypertension.compositeyes  6.505e+01  6.940e+01   0.937   0.3499  
DiabetesStatusDiabetes    -7.767e+01  5.685e+01  -1.366   0.1735  
SmokerStatusEx-smoker     -8.279e+01  5.080e+01  -1.630   0.1049  
SmokerStatusNever smoked  -1.082e+02  6.673e+01  -1.622   0.1065  
Med.Statin.LLDyes          2.056e+01  5.100e+01   0.403   0.6874  
Med.all.antiplateletyes   -2.819e+01  7.968e+01  -0.354   0.7239  
GFR_MDRD                  -2.649e-01  1.227e+00  -0.216   0.8294  
BMI                        2.449e+00  5.727e+00   0.428   0.6694  
MedHx_CVDNo                6.025e+01  4.653e+01   1.295   0.1970  
stenose50-70%              1.293e+02  3.595e+02   0.360   0.7195  
stenose70-90%              2.107e+02  3.119e+02   0.676   0.5001  
stenose90-99%              1.997e+02  3.107e+02   0.643   0.5211  
stenose100% (Occlusion)    3.900e+02  3.927e+02   0.993   0.3219  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 302.4 on 184 degrees of freedom
Multiple R-squared:  0.07645,   Adjusted R-squared:  -0.008876 
F-statistic: 0.896 on 17 and 184 DF,  p-value: 0.5795

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: PARC_rank 
Effect size...............: 48.42948 
Standard error............: 24.25197 
Odds ratio (effect size)..: 1.078096e+21 
Lower 95% CI..............: 2.449 
Upper 95% CI..............: 4.746238e+41 
T-value...................: 1.99693 
P-value...................: 0.04730674 
R^2.......................: 0.076452 
Adjusted r^2..............: -0.008876 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MDC_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      213.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-272.66 -136.16  -71.55   22.83 1915.88 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.381e+03  9.396e+02  -1.470    0.143
currentDF[, TRAIT]         3.086e+01  2.470e+01   1.250    0.213
Age                        2.608e+00  3.237e+00   0.806    0.422
Gendermale                 8.557e+01  5.573e+01   1.535    0.127
ORdate_epoch               8.779e-02  6.272e-02   1.400    0.163
Hypertension.compositeyes  8.475e+01  7.344e+01   1.154    0.250
DiabetesStatusDiabetes    -7.412e+01  6.109e+01  -1.213    0.227
SmokerStatusEx-smoker     -7.518e+01  5.401e+01  -1.392    0.166
SmokerStatusNever smoked  -1.009e+02  6.954e+01  -1.451    0.149
Med.Statin.LLDyes          2.374e+01  5.480e+01   0.433    0.665
Med.all.antiplateletyes   -1.800e+00  9.118e+01  -0.020    0.984
GFR_MDRD                  -1.460e-01  1.261e+00  -0.116    0.908
BMI                        9.592e-01  6.027e+00   0.159    0.874
MedHx_CVDNo                6.480e+01  5.029e+01   1.289    0.199
stenose50-70%              3.273e+01  3.947e+02   0.083    0.934
stenose70-90%              2.019e+02  3.189e+02   0.633    0.528
stenose90-99%              1.779e+02  3.178e+02   0.560    0.576
stenose100% (Occlusion)    3.455e+02  4.019e+02   0.860    0.391

Residual standard error: 308 on 171 degrees of freedom
Multiple R-squared:  0.06725,   Adjusted R-squared:  -0.02548 
F-statistic: 0.7252 on 17 and 171 DF,  p-value: 0.7742

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MDC_rank 
Effect size...............: 30.86294 
Standard error............: 24.69703 
Odds ratio (effect size)..: 2.532825e+13 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.667721e+34 
T-value...................: 1.249662 
P-value...................: 0.2131307 
R^2.......................: 0.067247 
Adjusted r^2..............: -0.025482 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing OPG_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      211.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-275.05 -143.48  -69.30   26.87 1890.52 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1259.6408   884.8948  -1.423    0.156  
currentDF[, TRAIT]           40.4710    22.1294   1.829    0.069 .
Age                           3.7454     3.1247   1.199    0.232  
Gendermale                   66.2998    52.6386   1.260    0.209  
ORdate_epoch                  0.0773     0.0584   1.324    0.187  
Hypertension.compositeyes    54.3780    68.9375   0.789    0.431  
DiabetesStatusDiabetes      -82.1583    56.8530  -1.445    0.150  
SmokerStatusEx-smoker       -91.9595    51.3558  -1.791    0.075 .
SmokerStatusNever smoked   -104.1995    66.6578  -1.563    0.120  
Med.Statin.LLDyes            20.4220    51.0985   0.400    0.690  
Med.all.antiplateletyes     -51.4252    80.0123  -0.643    0.521  
GFR_MDRD                     -0.5798     1.2194  -0.476    0.635  
BMI                           2.1802     5.7277   0.381    0.704  
MedHx_CVDNo                  63.0706    46.5793   1.354    0.177  
stenose50-70%               113.1068   360.2023   0.314    0.754  
stenose70-90%               230.2347   312.3865   0.737    0.462  
stenose90-99%               205.6173   311.2289   0.661    0.510  
stenose100% (Occlusion)     385.8549   393.3993   0.981    0.328  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 303 on 184 degrees of freedom
Multiple R-squared:  0.07328,   Adjusted R-squared:  -0.01234 
F-statistic: 0.8559 on 17 and 184 DF,  p-value: 0.6267

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: OPG_rank 
Effect size...............: 40.47104 
Standard error............: 22.12942 
Odds ratio (effect size)..: 3.770081e+17 
Lower 95% CI..............: 0.055 
Upper 95% CI..............: 2.58998e+36 
T-value...................: 1.828834 
P-value...................: 0.06904338 
R^2.......................: 0.073281 
Adjusted r^2..............: -0.012339 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing sICAM1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      211.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-267.70 -131.57  -76.38   32.01 1975.79 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.135e+03  9.152e+02  -1.240    0.216
currentDF[, TRAIT]        -2.627e+00  2.347e+01  -0.112    0.911
Age                        2.710e+00  3.171e+00   0.854    0.394
Gendermale                 7.248e+01  5.304e+01   1.366    0.174
ORdate_epoch               7.351e-02  6.080e-02   1.209    0.228
Hypertension.compositeyes  4.280e+01  6.969e+01   0.614    0.540
DiabetesStatusDiabetes    -8.536e+01  5.751e+01  -1.484    0.139
SmokerStatusEx-smoker     -7.852e+01  5.134e+01  -1.529    0.128
SmokerStatusNever smoked  -8.617e+01  6.766e+01  -1.274    0.204
Med.Statin.LLDyes          2.523e+01  5.150e+01   0.490    0.625
Med.all.antiplateletyes   -3.750e+01  8.043e+01  -0.466    0.642
GFR_MDRD                  -5.937e-01  1.236e+00  -0.480    0.632
BMI                        1.283e+00  5.760e+00   0.223    0.824
MedHx_CVDNo                6.295e+01  4.730e+01   1.331    0.185
stenose50-70%              1.316e+02  3.637e+02   0.362    0.718
stenose70-90%              2.261e+02  3.166e+02   0.714    0.476
stenose90-99%              2.085e+02  3.153e+02   0.661    0.509
stenose100% (Occlusion)    3.318e+02  3.964e+02   0.837    0.404

Residual standard error: 305.7 on 184 degrees of freedom
Multiple R-squared:  0.0565,    Adjusted R-squared:  -0.03067 
F-statistic: 0.6482 on 17 and 184 DF,  p-value: 0.8496

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -2.626836 
Standard error............: 23.47307 
Odds ratio (effect size)..: 0.072 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.916092e+18 
T-value...................: -0.111909 
P-value...................: 0.9110179 
R^2.......................: 0.0565 
Adjusted r^2..............: -0.030671 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing VEGFA_rank
filter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      210.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-276.30 -135.41  -71.76   23.14 2007.12 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -853.61108  932.86902  -0.915    0.362
currentDF[, TRAIT]          -2.04485   24.97769  -0.082    0.935
Age                          2.59380    3.26903   0.793    0.429
Gendermale                  80.22425   55.42530   1.447    0.150
ORdate_epoch                 0.06808    0.06934   0.982    0.328
Hypertension.compositeyes   32.40750   74.05453   0.438    0.662
DiabetesStatusDiabetes     -76.07754   59.46348  -1.279    0.203
SmokerStatusEx-smoker      -49.41605   52.86722  -0.935    0.351
SmokerStatusNever smoked   -62.12856   72.40528  -0.858    0.392
Med.Statin.LLDyes           16.27838   53.14466   0.306    0.760
Med.all.antiplateletyes    -33.22609   79.37878  -0.419    0.676
GFR_MDRD                    -0.72574    1.17502  -0.618    0.538
BMI                         -0.65980    6.07950  -0.109    0.914
MedHx_CVDNo                 48.78172   49.35907   0.988    0.324
stenose70-90%               91.13613  227.69293   0.400    0.689
stenose90-99%               46.24330  225.57040   0.205    0.838
stenose100% (Occlusion)    168.64747  321.12503   0.525    0.600

Residual standard error: 296.9 on 160 degrees of freedom
Multiple R-squared:  0.04777,   Adjusted R-squared:  -0.04746 
F-statistic: 0.5016 on 16 and 160 DF,  p-value: 0.9437

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -2.044846 
Standard error............: 24.97769 
Odds ratio (effect size)..: 0.129 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.362516e+20 
T-value...................: -0.081867 
P-value...................: 0.9348549 
R^2.......................: 0.047767 
Adjusted r^2..............: -0.047456 
Sample size of AE DB......: 622 
Sample size of model......: 177 
Missing data %............: 71.54341 

- processing TGFB_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes  
                229.98                  -76.53  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-266.43 -137.16  -71.91   34.77 1977.05 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -1.142e+03  8.825e+02  -1.294    0.197
currentDF[, TRAIT]         3.206e+00  2.240e+01   0.143    0.886
Age                        2.810e+00  3.129e+00   0.898    0.370
Gendermale                 6.952e+01  5.250e+01   1.324    0.187
ORdate_epoch               7.564e-02  5.861e-02   1.291    0.198
Hypertension.compositeyes  5.100e+01  6.825e+01   0.747    0.456
DiabetesStatusDiabetes    -8.887e+01  5.565e+01  -1.597    0.112
SmokerStatusEx-smoker     -7.190e+01  5.112e+01  -1.406    0.161
SmokerStatusNever smoked  -8.401e+01  6.781e+01  -1.239    0.217
Med.Statin.LLDyes          2.976e+01  5.130e+01   0.580    0.563
Med.all.antiplateletyes   -5.007e+01  8.245e+01  -0.607    0.544
GFR_MDRD                  -5.982e-01  1.223e+00  -0.489    0.625
BMI                        7.023e-01  5.789e+00   0.121    0.904
MedHx_CVDNo                6.149e+01  4.682e+01   1.313    0.191
stenose50-70%              1.298e+02  3.645e+02   0.356    0.722
stenose70-90%              2.205e+02  3.148e+02   0.701    0.484
stenose90-99%              2.021e+02  3.138e+02   0.644    0.520
stenose100% (Occlusion)    3.088e+02  3.980e+02   0.776    0.439

Residual standard error: 304.6 on 185 degrees of freedom
Multiple R-squared:  0.05856,   Adjusted R-squared:  -0.02795 
F-statistic: 0.6769 on 17 and 185 DF,  p-value: 0.8232

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: TGFB_rank 
Effect size...............: 3.20586 
Standard error............: 22.40001 
Odds ratio (effect size)..: 24.677 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.881106e+20 
T-value...................: 0.143119 
P-value...................: 0.8863522 
R^2.......................: 0.058556 
Adjusted r^2..............: -0.027955 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing MMP2_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      200.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-231.09 -124.15  -69.20   31.07 2032.68 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -465.58466  837.90888  -0.556    0.579
currentDF[, TRAIT]          -4.91769   21.38868  -0.230    0.818
Age                          0.92295    2.85224   0.324    0.747
Gendermale                  50.27334   48.24810   1.042    0.299
ORdate_epoch                 0.04232    0.05531   0.765    0.445
Hypertension.compositeyes   28.85110   65.23252   0.442    0.659
DiabetesStatusDiabetes     -75.75530   52.23887  -1.450    0.149
SmokerStatusEx-smoker      -35.14394   47.25791  -0.744    0.458
SmokerStatusNever smoked   -42.25281   62.39851  -0.677    0.499
Med.Statin.LLDyes            7.63101   46.93408   0.163    0.871
Med.all.antiplateletyes    -45.77690   73.69231  -0.621    0.535
GFR_MDRD                    -1.11466    1.09337  -1.019    0.309
BMI                         -0.53654    5.43725  -0.099    0.922
MedHx_CVDNo                 43.21482   43.28448   0.998    0.319
stenose50-70%              105.44204  333.63757   0.316    0.752
stenose70-90%              177.08739  289.63336   0.611    0.542
stenose90-99%              153.79478  288.38776   0.533    0.594
stenose100% (Occlusion)    258.64560  363.83377   0.711    0.478

Residual standard error: 280.4 on 184 degrees of freedom
Multiple R-squared:  0.04077,   Adjusted R-squared:  -0.04785 
F-statistic: 0.4601 on 17 and 184 DF,  p-value: 0.9672

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MMP2_rank 
Effect size...............: -4.917694 
Standard error............: 21.38868 
Odds ratio (effect size)..: 0.007 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.176749e+16 
T-value...................: -0.22992 
P-value...................: 0.818409 
R^2.......................: 0.040772 
Adjusted r^2..............: -0.047852 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP8_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      200.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-246.06 -126.45  -69.60   23.11 2034.61 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -468.09964  824.30055  -0.568    0.571
currentDF[, TRAIT]           9.94572   20.65085   0.482    0.631
Age                          1.07830    2.84236   0.379    0.705
Gendermale                  47.65535   48.55660   0.981    0.328
ORdate_epoch                 0.04356    0.05441   0.800    0.424
Hypertension.compositeyes   31.16563   64.36641   0.484    0.629
DiabetesStatusDiabetes     -72.82029   52.61217  -1.384    0.168
SmokerStatusEx-smoker      -34.66688   47.17559  -0.735    0.463
SmokerStatusNever smoked   -43.98859   61.80910  -0.712    0.478
Med.Statin.LLDyes            7.87987   46.90937   0.168    0.867
Med.all.antiplateletyes    -45.09910   73.59546  -0.613    0.541
GFR_MDRD                    -1.00036    1.09678  -0.912    0.363
BMI                         -0.66604    5.43975  -0.122    0.903
MedHx_CVDNo                 45.57016   43.46050   1.049    0.296
stenose50-70%               80.42648  335.87444   0.239    0.811
stenose70-90%              144.55833  296.20326   0.488    0.626
stenose90-99%              124.75982  293.80101   0.425    0.672
stenose100% (Occlusion)    225.59945  370.16373   0.609    0.543

Residual standard error: 280.3 on 184 degrees of freedom
Multiple R-squared:  0.0417,    Adjusted R-squared:  -0.04683 
F-statistic: 0.471 on 17 and 184 DF,  p-value: 0.9631

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MMP8_rank 
Effect size...............: 9.945719 
Standard error............: 20.65085 
Odds ratio (effect size)..: 20862.71 
Lower 95% CI..............: 0 
Upper 95% CI..............: 7.901869e+21 
T-value...................: 0.481613 
P-value...................: 0.6306532 
R^2.......................: 0.041704 
Adjusted r^2..............: -0.046834 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP9_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             201.1                30.0  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-273.42 -128.36  -65.20   32.59 2028.74 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -664.33751  821.91836  -0.808    0.420
currentDF[, TRAIT]          32.54947   20.26581   1.606    0.110
Age                          1.45634    2.83339   0.514    0.608
Gendermale                  46.58506   47.63064   0.978    0.329
ORdate_epoch                 0.05525    0.05443   1.015    0.311
Hypertension.compositeyes   48.93620   64.90169   0.754    0.452
DiabetesStatusDiabetes     -65.45119   52.28741  -1.252    0.212
SmokerStatusEx-smoker      -37.95686   46.81684  -0.811    0.419
SmokerStatusNever smoked   -51.40366   61.58268  -0.835    0.405
Med.Statin.LLDyes            5.01770   46.64347   0.108    0.914
Med.all.antiplateletyes    -54.41258   73.19086  -0.743    0.458
GFR_MDRD                    -0.77069    1.09406  -0.704    0.482
BMI                         -0.84285    5.39218  -0.156    0.876
MedHx_CVDNo                 49.43915   43.13247   1.146    0.253
stenose50-70%               91.20286  330.98423   0.276    0.783
stenose70-90%              152.72448  287.86685   0.531    0.596
stenose90-99%              133.25998  286.60278   0.465    0.643
stenose100% (Occlusion)    251.74081  361.38764   0.697    0.487

Residual standard error: 278.5 on 184 degrees of freedom
Multiple R-squared:  0.05376,   Adjusted R-squared:  -0.03366 
F-statistic: 0.615 on 17 and 184 DF,  p-value: 0.8777

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A1 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A1 
Trait/outcome.............: MMP9_rank 
Effect size...............: 32.54948 
Standard error............: 20.26581 
Odds ratio (effect size)..: 1.367909e+14 
Lower 95% CI..............: 0.001 
Upper 95% CI..............: 2.435944e+31 
T-value...................: 1.606127 
P-value...................: 0.1099608 
R^2.......................: 0.053762 
Adjusted r^2..............: -0.033662 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

Analysis of COL4A2.

- processing IL2_rank
filter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)

Coefficients:
            (Intercept)            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                 225.00                   309.49                    62.92                  1512.00  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1681.7  -300.1  -123.1    43.3  7218.9 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2501.6125  2628.8530  -0.952   0.3429  
currentDF[, TRAIT]          -33.0799    67.9392  -0.487   0.6271  
Age                           4.8097     9.0747   0.530   0.5969  
Gendermale                  149.2334   163.2505   0.914   0.3622  
ORdate_epoch                  0.1752     0.1875   0.934   0.3517  
Hypertension.compositeyes    10.8280   212.5504   0.051   0.9594  
DiabetesStatusDiabetes      -49.5250   178.1017  -0.278   0.7814  
SmokerStatusEx-smoker        47.3802   152.6352   0.310   0.7567  
SmokerStatusNever smoked     53.5591   207.8216   0.258   0.7970  
Med.Statin.LLDyes            12.1729   149.9420   0.081   0.9354  
Med.all.antiplateletyes     105.9194   237.6000   0.446   0.6564  
GFR_MDRD                      0.1297     3.8992   0.033   0.9735  
BMI                           0.2824    20.2305   0.014   0.9889  
MedHx_CVDNo                 -69.8939   141.8589  -0.493   0.6230  
stenose70-90%               309.4779   606.9704   0.510   0.6109  
stenose90-99%                31.2490   603.0835   0.052   0.9587  
stenose100% (Occlusion)    1647.7446   878.4030   1.876   0.0627 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 809.5 on 146 degrees of freedom
Multiple R-squared:  0.08273,   Adjusted R-squared:  -0.01779 
F-statistic: 0.823 on 16 and 146 DF,  p-value: 0.658

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL2_rank 
Effect size...............: -33.07994 
Standard error............: 67.93922 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.914711e+43 
T-value...................: -0.486905 
P-value...................: 0.6270559 
R^2.......................: 0.082729 
Adjusted r^2..............: -0.017793 
Sample size of AE DB......: 622 
Sample size of model......: 163 
Missing data %............: 73.79421 

- processing IL4_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -4324.9485        0.3853  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1376.3  -393.9  -202.2    91.4  7460.9 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -4040.5964  3824.8141  -1.056    0.293
currentDF[, TRAIT]           -9.7107    98.7492  -0.098    0.922
Age                           5.8298    12.9428   0.450    0.653
Gendermale                  321.5479   231.1930   1.391    0.167
ORdate_epoch                  0.3514     0.2744   1.281    0.203
Hypertension.compositeyes   128.2265   303.6725   0.422    0.674
DiabetesStatusDiabetes     -220.6529   252.5974  -0.874    0.384
SmokerStatusEx-smoker      -129.9907   210.7223  -0.617    0.538
SmokerStatusNever smoked   -106.6742   299.4329  -0.356    0.722
Med.Statin.LLDyes            69.1156   221.0163   0.313    0.755
Med.all.antiplateletyes      89.7512   331.7409   0.271    0.787
GFR_MDRD                     -6.0817     6.0269  -1.009    0.315
BMI                         -10.5330    30.1829  -0.349    0.728
MedHx_CVDNo                 149.9160   201.0947   0.745    0.457
stenose70-90%               227.4481   818.0985   0.278    0.781
stenose90-99%               -99.8655   811.5614  -0.123    0.902
stenose100% (Occlusion)    1461.8167  1185.7937   1.233    0.220

Residual standard error: 1078 on 128 degrees of freedom
Multiple R-squared:  0.08954,   Adjusted R-squared:  -0.02427 
F-statistic: 0.7868 on 16 and 128 DF,  p-value: 0.6981

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL4_rank 
Effect size...............: -9.710658 
Standard error............: 98.74917 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.913609e+79 
T-value...................: -0.098337 
P-value...................: 0.9218188 
R^2.......................: 0.08954 
Adjusted r^2..............: -0.024268 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing IL5_rank
filter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -4031.1765        0.3607  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1419.0  -385.9  -194.0    60.4  7549.1 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -3697.5807  3478.6394  -1.063    0.290
currentDF[, TRAIT]          -48.0210    88.6924  -0.541    0.589
Age                           8.0039    12.2757   0.652    0.515
Gendermale                  285.3810   214.0887   1.333    0.185
ORdate_epoch                  0.2773     0.2538   1.093    0.276
Hypertension.compositeyes    45.4001   283.6855   0.160    0.873
DiabetesStatusDiabetes     -149.0223   230.6014  -0.646    0.519
SmokerStatusEx-smoker      -157.6038   195.0234  -0.808    0.420
SmokerStatusNever smoked   -139.5908   281.6488  -0.496    0.621
Med.Statin.LLDyes           117.6645   200.6396   0.586    0.559
Med.all.antiplateletyes     151.9112   310.7940   0.489    0.626
GFR_MDRD                     -3.0049     5.3296  -0.564    0.574
BMI                          -8.1261    26.4579  -0.307    0.759
MedHx_CVDNo                 129.3973   189.2517   0.684    0.495
stenose70-90%               345.4986   655.0564   0.527    0.599
stenose90-99%                84.6892   647.0581   0.131    0.896
stenose100% (Occlusion)    1634.7890  1045.7467   1.563    0.120

Residual standard error: 1048 on 141 degrees of freedom
Multiple R-squared:  0.07785,   Adjusted R-squared:  -0.02679 
F-statistic: 0.744 on 16 and 141 DF,  p-value: 0.7449

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL5_rank 
Effect size...............: -48.02103 
Standard error............: 88.69235 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.377116e+54 
T-value...................: -0.541434 
P-value...................: 0.5890632 
R^2.......................: 0.077853 
Adjusted r^2..............: -0.026788 
Sample size of AE DB......: 622 
Sample size of model......: 158 
Missing data %............: 74.59807 

- processing IL6_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      427.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1467.0  -362.5  -181.0     9.9  7652.4 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -3208.2820  3472.0149  -0.924    0.357
currentDF[, TRAIT]           -5.9975    83.2065  -0.072    0.943
Age                           7.5599    12.1620   0.622    0.535
Gendermale                  253.0957   200.1601   1.264    0.208
ORdate_epoch                  0.1788     0.2313   0.773    0.441
Hypertension.compositeyes    27.1787   267.4361   0.102    0.919
DiabetesStatusDiabetes     -210.4835   223.7633  -0.941    0.348
SmokerStatusEx-smoker       -91.9170   191.9707  -0.479    0.633
SmokerStatusNever smoked   -125.9001   266.3449  -0.473    0.637
Med.Statin.LLDyes           134.1203   189.2135   0.709    0.480
Med.all.antiplateletyes     116.0371   306.8386   0.378    0.706
GFR_MDRD                     -3.0664     5.1714  -0.593    0.554
BMI                           3.1478    22.1815   0.142    0.887
MedHx_CVDNo                 138.7382   180.4067   0.769    0.443
stenose50-70%               481.3898  1234.2421   0.390    0.697
stenose70-90%               791.1215  1072.3208   0.738    0.462
stenose90-99%               579.3810  1070.3555   0.541    0.589
stenose100% (Occlusion)    2175.1721  1351.1516   1.610    0.110

Residual standard error: 1029 on 146 degrees of freedom
Multiple R-squared:  0.0646,    Adjusted R-squared:  -0.04432 
F-statistic: 0.5931 on 17 and 146 DF,  p-value: 0.8931

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL6_rank 
Effect size...............: -5.997544 
Standard error............: 83.20645 
Odds ratio (effect size)..: 0.002 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.667492e+68 
T-value...................: -0.07208 
P-value...................: 0.9426367 
R^2.......................: 0.064597 
Adjusted r^2..............: -0.04432 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL8_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + stenose, data = currentDF)

Coefficients:
            (Intercept)                 GFR_MDRD            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                355.326                   -4.939                  306.338                  378.294                  340.667                 3541.356  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-610.2 -306.3 -126.0   72.1 7689.8 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -2649.3536  2894.2286  -0.915  0.36161   
currentDF[, TRAIT]          -24.2152    69.9452  -0.346  0.72973   
Age                          -3.8034     9.4662  -0.402  0.68847   
Gendermale                  156.2782   169.0244   0.925  0.35682   
ORdate_epoch                  0.2377     0.1926   1.234  0.21930   
Hypertension.compositeyes  -140.3933   209.6624  -0.670  0.50424   
DiabetesStatusDiabetes     -150.3669   177.6069  -0.847  0.39869   
SmokerStatusEx-smoker       -36.0349   158.6794  -0.227  0.82069   
SmokerStatusNever smoked    -92.5953   228.3700  -0.405  0.68578   
Med.Statin.LLDyes            29.6508   151.9579   0.195  0.84559   
Med.all.antiplateletyes     253.2617   234.1191   1.082  0.28127   
GFR_MDRD                     -6.3144     3.7443  -1.686  0.09401 . 
BMI                          -3.2283    17.8906  -0.180  0.85707   
MedHx_CVDNo                 120.0848   148.1224   0.811  0.41895   
stenose50-70%               451.8595   969.9950   0.466  0.64208   
stenose70-90%               623.4309   847.1267   0.736  0.46304   
stenose90-99%               595.2435   844.0454   0.705  0.48188   
stenose100% (Occlusion)    3763.4335  1205.0194   3.123  0.00219 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 809.7 on 136 degrees of freedom
Multiple R-squared:  0.1428,    Adjusted R-squared:  0.03564 
F-statistic: 1.333 on 17 and 136 DF,  p-value: 0.1816

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL8_rank 
Effect size...............: -24.21521 
Standard error............: 69.94522 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.052064e+49 
T-value...................: -0.346202 
P-value...................: 0.7297256 
R^2.......................: 0.142792 
Adjusted r^2..............: 0.03564 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing IL9_rank
filter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)

Coefficients:
(Intercept)  MedHx_CVDNo  
      360.1        228.0  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1185.4  -355.3  -169.9    90.9  7241.9 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4724.665   3113.410  -1.518   0.1310  
currentDF[, TRAIT]          126.809     78.403   1.617   0.1077  
Age                          16.429     11.411   1.440   0.1518  
Gendermale                  238.546    189.157   1.261   0.2090  
ORdate_epoch                  0.225      0.204   1.103   0.2716  
Hypertension.compositeyes   243.512    255.410   0.953   0.3418  
DiabetesStatusDiabetes     -233.128    211.409  -1.103   0.2717  
SmokerStatusEx-smoker      -280.450    184.791  -1.518   0.1310  
SmokerStatusNever smoked   -362.908    235.309  -1.542   0.1249  
Med.Statin.LLDyes           236.002    186.855   1.263   0.2083  
Med.all.antiplateletyes      87.301    308.911   0.283   0.7778  
GFR_MDRD                     -1.170      4.299  -0.272   0.7858  
BMI                           3.942     20.815   0.189   0.8500  
MedHx_CVDNo                 271.008    170.796   1.587   0.1145  
stenose50-70%               350.835   1329.312   0.264   0.7922  
stenose70-90%               800.848   1077.246   0.743   0.4583  
stenose90-99%               611.354   1072.708   0.570   0.5695  
stenose100% (Occlusion)    2433.342   1364.513   1.783   0.0763 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1042 on 168 degrees of freedom
Multiple R-squared:  0.09611,   Adjusted R-squared:  0.004649 
F-statistic: 1.051 on 17 and 168 DF,  p-value: 0.4066

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL9_rank 
Effect size...............: 126.8092 
Standard error............: 78.40329 
Odds ratio (effect size)..: 1.181802e+55 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.468027e+121 
T-value...................: 1.617397 
P-value...................: 0.1076687 
R^2.......................: 0.096114 
Adjusted r^2..............: 0.004649 
Sample size of AE DB......: 622 
Sample size of model......: 186 
Missing data %............: 70.09646 

- processing IL10_rank
filter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)

Coefficients:
            (Intercept)            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                 225.00                   373.82                    34.03                  1512.00  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1633.9  -318.8  -134.7    84.8  7101.6 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -3936.3327  3226.9374  -1.220   0.2249  
currentDF[, TRAIT]           29.2530    78.3503   0.373   0.7095  
Age                           7.0478    10.5281   0.669   0.5045  
Gendermale                  199.8737   185.5494   1.077   0.2835  
ORdate_epoch                  0.2594     0.2258   1.149   0.2530  
Hypertension.compositeyes    13.4826   245.3179   0.055   0.9563  
DiabetesStatusDiabetes     -166.7983   202.6543  -0.823   0.4121  
SmokerStatusEx-smoker        38.9089   171.2913   0.227   0.8207  
SmokerStatusNever smoked     56.9488   238.3795   0.239   0.8116  
Med.Statin.LLDyes            42.1924   170.8276   0.247   0.8053  
Med.all.antiplateletyes     125.5794   265.5669   0.473   0.6371  
GFR_MDRD                     -0.5195     4.8689  -0.107   0.9152  
BMI                           7.7115    24.0529   0.321   0.7491  
MedHx_CVDNo                 -34.1390   168.0457  -0.203   0.8394  
stenose70-90%               403.7347   648.1618   0.623   0.5345  
stenose90-99%                35.8665   642.9860   0.056   0.9556  
stenose100% (Occlusion)    1747.3279   940.4624   1.858   0.0656 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 851.5 on 122 degrees of freedom
Multiple R-squared:  0.1074,    Adjusted R-squared:  -0.009713 
F-statistic: 0.917 on 16 and 122 DF,  p-value: 0.5516

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL10_rank 
Effect size...............: 29.25298 
Standard error............: 78.35031 
Odds ratio (effect size)..: 5.062981e+12 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.49767e+79 
T-value...................: 0.373361 
P-value...................: 0.7095275 
R^2.......................: 0.107355 
Adjusted r^2..............: -0.009713 
Sample size of AE DB......: 622 
Sample size of model......: 139 
Missing data %............: 77.65273 

- processing IL12_rank
filter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + stenose, data = currentDF)

Coefficients:
            (Intercept)               Gendermale            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                 -68.96                   293.96                   456.53                   204.34                  3216.00  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-834.2 -417.5 -205.3   22.9 7578.0 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2722.8744  3968.6535  -0.686   0.4939  
currentDF[, TRAIT]          -60.3070    96.5485  -0.625   0.5333  
Age                           8.0539    13.3194   0.605   0.5465  
Gendermale                  303.6783   228.3443   1.330   0.1859  
ORdate_epoch                  0.2269     0.2763   0.821   0.4132  
Hypertension.compositeyes   -17.4399   310.5712  -0.056   0.9553  
DiabetesStatusDiabetes     -164.0281   247.4328  -0.663   0.5086  
SmokerStatusEx-smoker      -144.0609   219.5895  -0.656   0.5130  
SmokerStatusNever smoked   -111.8267   291.3739  -0.384   0.7018  
Med.Statin.LLDyes            41.4184   218.5677   0.189   0.8500  
Med.all.antiplateletyes     152.5472   332.1907   0.459   0.6469  
GFR_MDRD                     -2.8861     5.8791  -0.491   0.6243  
BMI                         -13.0590    29.7487  -0.439   0.6614  
MedHx_CVDNo                  29.9489   200.4019   0.149   0.8814  
stenose70-90%               269.3055   813.4363   0.331   0.7411  
stenose90-99%               -26.0662   810.1864  -0.032   0.9744  
stenose100% (Occlusion)    3046.0734  1425.2712   2.137   0.0345 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1074 on 129 degrees of freedom
Multiple R-squared:  0.1061,    Adjusted R-squared:  -0.00474 
F-statistic: 0.9572 on 16 and 129 DF,  p-value: 0.507

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL12_rank 
Effect size...............: -60.307 
Standard error............: 96.54849 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.834157e+55 
T-value...................: -0.624629 
P-value...................: 0.5333177 
R^2.......................: 0.106128 
Adjusted r^2..............: -0.00474 
Sample size of AE DB......: 622 
Sample size of model......: 146 
Missing data %............: 76.52733 

- processing IL13_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      444.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1271.3  -407.7  -185.6    93.5  7214.6 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4655.3054  3007.3924  -1.548   0.1234  
currentDF[, TRAIT]          143.3603    77.4742   1.850   0.0659 .
Age                          20.0666    10.6950   1.876   0.0622 .
Gendermale                  244.2975   179.3815   1.362   0.1749  
ORdate_epoch                  0.2062     0.1995   1.034   0.3027  
Hypertension.compositeyes   128.8749   234.2430   0.550   0.5829  
DiabetesStatusDiabetes     -161.5251   193.9284  -0.833   0.4060  
SmokerStatusEx-smoker      -287.6631   174.9650  -1.644   0.1019  
SmokerStatusNever smoked   -376.6892   228.9869  -1.645   0.1017  
Med.Statin.LLDyes           166.9790   173.9497   0.960   0.3384  
Med.all.antiplateletyes     116.3788   270.8672   0.430   0.6680  
GFR_MDRD                      0.2319     4.1465   0.056   0.9555  
BMI                           7.3970    19.4089   0.381   0.7036  
MedHx_CVDNo                 214.3950   158.5741   1.352   0.1780  
stenose50-70%               290.6668  1229.2260   0.236   0.8133  
stenose70-90%               675.3959  1064.1709   0.635   0.5264  
stenose90-99%               470.4055  1062.9751   0.443   0.6586  
stenose100% (Occlusion)    2317.2603  1332.6294   1.739   0.0837 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1029 on 184 degrees of freedom
Multiple R-squared:  0.08783,   Adjusted R-squared:  0.003554 
F-statistic: 1.042 on 17 and 184 DF,  p-value: 0.4149

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL13_rank 
Effect size...............: 143.3603 
Standard error............: 77.4742 
Odds ratio (effect size)..: 1.822162e+62 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.614199e+128 
T-value...................: 1.850426 
P-value...................: 0.06585545 
R^2.......................: 0.087831 
Adjusted r^2..............: 0.003554 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing IL21_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      444.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1275.3  -397.1  -219.6    76.9  7330.9 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4766.2965  3024.4945  -1.576   0.1168  
currentDF[, TRAIT]           92.2995    77.2788   1.194   0.2339  
Age                          18.0083    10.6568   1.690   0.0928 .
Gendermale                  251.9890   180.6751   1.395   0.1648  
ORdate_epoch                  0.2247     0.2002   1.122   0.2632  
Hypertension.compositeyes   105.1841   234.9072   0.448   0.6548  
DiabetesStatusDiabetes     -177.0530   194.6910  -0.909   0.3643  
SmokerStatusEx-smoker      -260.4402   174.8468  -1.490   0.1381  
SmokerStatusNever smoked   -340.5608   229.2075  -1.486   0.1390  
Med.Statin.LLDyes           172.1672   175.1532   0.983   0.3269  
Med.all.antiplateletyes     117.8396   272.4860   0.432   0.6659  
GFR_MDRD                     -0.0426     4.1657  -0.010   0.9919  
BMI                           6.2352    19.4967   0.320   0.7495  
MedHx_CVDNo                 205.9401   159.3268   1.293   0.1978  
stenose50-70%               386.6569  1234.3726   0.313   0.7545  
stenose70-90%               734.2594  1069.3727   0.687   0.4932  
stenose90-99%               547.0274  1068.1134   0.512   0.6092  
stenose100% (Occlusion)    2284.7701  1340.0028   1.705   0.0899 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1035 on 184 degrees of freedom
Multiple R-squared:  0.078, Adjusted R-squared:  -0.00718 
F-statistic: 0.9157 on 17 and 184 DF,  p-value: 0.5564

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IL21_rank 
Effect size...............: 92.2995 
Standard error............: 77.27879 
Odds ratio (effect size)..: 1.216646e+40 
Lower 95% CI..............: 0 
Upper 95% CI..............: 7.348431e+105 
T-value...................: 1.19437 
P-value...................: 0.2338705 
R^2.......................: 0.078004 
Adjusted r^2..............: -0.00718 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing INFG_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)

Coefficients:
            (Intercept)            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                   83.0                    114.0                    480.8                    289.4                   3358.0  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-777.8 -381.6 -195.2   64.6 7567.1 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -5077.9708  3779.2762  -1.344   0.1813  
currentDF[, TRAIT]           82.3762    97.8521   0.842   0.4014  
Age                          13.2264    12.4570   1.062   0.2902  
Gendermale                  320.6863   227.3596   1.410   0.1607  
ORdate_epoch                  0.2992     0.2557   1.170   0.2440  
Hypertension.compositeyes   -41.4073   301.1394  -0.138   0.8908  
DiabetesStatusDiabetes     -191.2547   228.6785  -0.836   0.4044  
SmokerStatusEx-smoker      -126.9641   205.9636  -0.616   0.5386  
SmokerStatusNever smoked   -105.6481   279.9940  -0.377   0.7065  
Med.Statin.LLDyes           136.3332   203.5934   0.670   0.5042  
Med.all.antiplateletyes     235.8359   298.4149   0.790   0.4307  
GFR_MDRD                     -3.1246     5.0333  -0.621   0.5358  
BMI                           2.0256    23.2533   0.087   0.9307  
MedHx_CVDNo                 142.1832   196.2928   0.724   0.4701  
stenose50-70%               440.0374  1260.1605   0.349   0.7275  
stenose70-90%               787.1682  1099.3906   0.716   0.4752  
stenose90-99%               528.9281  1093.2896   0.484   0.6293  
stenose100% (Occlusion)    3746.4171  1553.7354   2.411   0.0172 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1049 on 136 degrees of freedom
Multiple R-squared:  0.106, Adjusted R-squared:  -0.005759 
F-statistic: 0.9485 on 17 and 136 DF,  p-value: 0.5195

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: INFG_rank 
Effect size...............: 82.37622 
Standard error............: 97.85213 
Odds ratio (effect size)..: 5.963997e+35 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.172071e+119 
T-value...................: 0.841844 
P-value...................: 0.4013532 
R^2.......................: 0.105992 
Adjusted r^2..............: -0.005759 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing TNFA_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ stenose, data = currentDF)

Coefficients:
            (Intercept)            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                  225.0                    359.6                    136.0                   3216.0  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-972.0 -395.6 -194.4   42.9 7544.0 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2170.2974  3798.2421  -0.571   0.5687  
currentDF[, TRAIT]          -72.9616    95.5175  -0.764   0.4464  
Age                           6.6590    12.9033   0.516   0.6067  
Gendermale                  289.5837   225.3926   1.285   0.2012  
ORdate_epoch                  0.2049     0.2647   0.774   0.4402  
Hypertension.compositeyes    -7.5794   302.0798  -0.025   0.9800  
DiabetesStatusDiabetes     -208.4305   243.9177  -0.855   0.3944  
SmokerStatusEx-smoker       -17.3869   210.2897  -0.083   0.9342  
SmokerStatusNever smoked    -93.7782   291.2743  -0.322   0.7480  
Med.Statin.LLDyes            15.4172   216.6515   0.071   0.9434  
Med.all.antiplateletyes     148.7800   325.1606   0.458   0.6480  
GFR_MDRD                     -2.6680     5.6028  -0.476   0.6347  
BMI                         -22.4468    27.7475  -0.809   0.4200  
MedHx_CVDNo                 104.6539   203.9283   0.513   0.6087  
stenose70-90%               273.7974   805.6030   0.340   0.7345  
stenose90-99%               -40.9069   797.4211  -0.051   0.9592  
stenose100% (Occlusion)    2980.6820  1405.0428   2.121   0.0358 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1065 on 128 degrees of freedom
Multiple R-squared:  0.1131,    Adjusted R-squared:  0.00221 
F-statistic:  1.02 on 16 and 128 DF,  p-value: 0.4404

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: TNFA_rank 
Effect size...............: -72.96163 
Standard error............: 95.51748 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.161891e+49 
T-value...................: -0.763856 
P-value...................: 0.4463586 
R^2.......................: 0.113075 
Adjusted r^2..............: 0.00221 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing MIF_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      444.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1330.4  -376.0  -208.3    73.7  7493.9 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4885.6128  3284.3855  -1.488   0.1386  
currentDF[, TRAIT]           -9.4424    89.9151  -0.105   0.9165  
Age                          15.8951    10.5985   1.500   0.1354  
Gendermale                  277.6973   180.3891   1.539   0.1254  
ORdate_epoch                  0.2341     0.2248   1.042   0.2989  
Hypertension.compositeyes    89.1227   237.1576   0.376   0.7075  
DiabetesStatusDiabetes     -195.6047   195.3229  -1.001   0.3179  
SmokerStatusEx-smoker      -237.8742   174.8202  -1.361   0.1753  
SmokerStatusNever smoked   -289.6426   227.5279  -1.273   0.2046  
Med.Statin.LLDyes           193.1387   175.2195   1.102   0.2718  
Med.all.antiplateletyes     136.2547   274.1882   0.497   0.6198  
GFR_MDRD                     -0.1407     4.2431  -0.033   0.9736  
BMI                           5.7938    19.5754   0.296   0.7676  
MedHx_CVDNo                 195.3036   160.0366   1.220   0.2239  
stenose50-70%               517.9147  1238.0878   0.418   0.6762  
stenose70-90%               827.3766  1075.2543   0.769   0.4426  
stenose90-99%               680.4576  1073.0365   0.634   0.5268  
stenose100% (Occlusion)    2320.3393  1346.5015   1.723   0.0865 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1039 on 184 degrees of freedom
Multiple R-squared:  0.07091,   Adjusted R-squared:  -0.01493 
F-statistic: 0.8261 on 17 and 184 DF,  p-value: 0.6616

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MIF_rank 
Effect size...............: -9.442425 
Standard error............: 89.91506 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.731807e+72 
T-value...................: -0.105015 
P-value...................: 0.9164783 
R^2.......................: 0.070912 
Adjusted r^2..............: -0.014928 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MCP1_rank
filter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      447.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1314.4  -375.3  -201.1    56.3  7508.2 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4.875e+03  3.134e+03  -1.555    0.122  
currentDF[, TRAIT]        -1.096e+01  7.716e+01  -0.142    0.887  
Age                        1.618e+01  1.077e+01   1.502    0.135  
Gendermale                 2.726e+02  1.837e+02   1.484    0.139  
ORdate_epoch               2.353e-01  2.074e-01   1.134    0.258  
Hypertension.compositeyes  1.025e+02  2.389e+02   0.429    0.668  
DiabetesStatusDiabetes    -2.001e+02  1.970e+02  -1.015    0.311  
SmokerStatusEx-smoker     -2.365e+02  1.759e+02  -1.345    0.180  
SmokerStatusNever smoked  -2.948e+02  2.303e+02  -1.280    0.202  
Med.Statin.LLDyes          1.919e+02  1.770e+02   1.084    0.280  
Med.all.antiplateletyes    9.804e+01  2.916e+02   0.336    0.737  
GFR_MDRD                   4.906e-02  4.224e+00   0.012    0.991  
BMI                        5.236e+00  1.999e+01   0.262    0.794  
MedHx_CVDNo                1.988e+02  1.617e+02   1.229    0.221  
stenose50-70%              5.114e+02  1.241e+03   0.412    0.681  
stenose70-90%              8.186e+02  1.077e+03   0.760    0.448  
stenose90-99%              6.669e+02  1.073e+03   0.621    0.535  
stenose100% (Occlusion)    2.269e+03  1.356e+03   1.673    0.096 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1044 on 182 degrees of freedom
Multiple R-squared:  0.07094,   Adjusted R-squared:  -0.01584 
F-statistic: 0.8174 on 17 and 182 DF,  p-value: 0.6717

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MCP1_rank 
Effect size...............: -10.96447 
Standard error............: 77.15834 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.254522e+60 
T-value...................: -0.142104 
P-value...................: 0.8871554 
R^2.......................: 0.070937 
Adjusted r^2..............: -0.015843 
Sample size of AE DB......: 622 
Sample size of model......: 200 
Missing data %............: 67.84566 

- processing MIP1a_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      451.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1290.9  -392.5  -185.5    85.5  7407.4 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -4359.4113  3149.7601  -1.384    0.168
currentDF[, TRAIT]           37.8969    80.8660   0.469    0.640
Age                          15.3290    11.2091   1.368    0.173
Gendermale                  272.7126   193.6671   1.408    0.161
ORdate_epoch                  0.2007     0.2078   0.966    0.336
Hypertension.compositeyes   115.7298   253.6959   0.456    0.649
DiabetesStatusDiabetes     -255.0180   209.1480  -1.219    0.224
SmokerStatusEx-smoker      -230.9273   187.4271  -1.232    0.220
SmokerStatusNever smoked   -325.1301   240.8216  -1.350    0.179
Med.Statin.LLDyes           187.1791   187.0813   1.001    0.318
Med.all.antiplateletyes     118.1858   314.0445   0.376    0.707
GFR_MDRD                     -0.5637     4.3727  -0.129    0.898
BMI                           6.9198    20.7072   0.334    0.739
MedHx_CVDNo                 239.7767   173.6540   1.381    0.169
stenose50-70%               415.6587  1361.9042   0.305    0.761
stenose70-90%               805.9705  1102.8193   0.731    0.466
stenose90-99%               592.5575  1101.0252   0.538    0.591
stenose100% (Occlusion)    2251.2464  1388.6008   1.621    0.107

Residual standard error: 1063 on 171 degrees of freedom
Multiple R-squared:  0.07613,   Adjusted R-squared:  -0.01572 
F-statistic: 0.8289 on 17 and 171 DF,  p-value: 0.6582

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 37.89693 
Standard error............: 80.86602 
Odds ratio (effect size)..: 2.873608e+16 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.963224e+85 
T-value...................: 0.468639 
P-value...................: 0.6399249 
R^2.......................: 0.07613 
Adjusted r^2..............: -0.015716 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing RANTES_rank
filter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      450.2  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1355.1  -381.3  -206.7    65.6  7389.5 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -5997.4559  3255.0275  -1.843   0.0670 .
currentDF[, TRAIT]           81.4031    85.4057   0.953   0.3418  
Age                          18.2564    10.8700   1.680   0.0948 .
Gendermale                  266.9110   185.9713   1.435   0.1530  
ORdate_epoch                  0.3135     0.2194   1.428   0.1549  
Hypertension.compositeyes   129.3402   246.6397   0.524   0.6006  
DiabetesStatusDiabetes     -163.0162   201.0730  -0.811   0.4186  
SmokerStatusEx-smoker      -238.6119   179.4048  -1.330   0.1852  
SmokerStatusNever smoked   -332.6214   232.0024  -1.434   0.1534  
Med.Statin.LLDyes           200.1896   177.9898   1.125   0.2622  
Med.all.antiplateletyes     119.8139   290.3458   0.413   0.6803  
GFR_MDRD                      0.3443     4.2430   0.081   0.9354  
BMI                           4.9205    20.0414   0.246   0.8063  
MedHx_CVDNo                 238.5466   166.4211   1.433   0.1535  
stenose50-70%               447.8425  1327.9225   0.337   0.7363  
stenose70-90%               750.5235  1083.3165   0.693   0.4893  
stenose90-99%               594.4201  1077.8441   0.551   0.5820  
stenose100% (Occlusion)    2208.0641  1366.2118   1.616   0.1078  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1046 on 180 degrees of freedom
Multiple R-squared:  0.07774,   Adjusted R-squared:  -0.009359 
F-statistic: 0.8926 on 17 and 180 DF,  p-value: 0.5836

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: RANTES_rank 
Effect size...............: 81.4031 
Standard error............: 85.40575 
Odds ratio (effect size)..: 2.253806e+35 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.126569e+108 
T-value...................: 0.953134 
P-value...................: 0.3418005 
R^2.......................: 0.077743 
Adjusted r^2..............: -0.009359 
Sample size of AE DB......: 622 
Sample size of model......: 198 
Missing data %............: 68.1672 

- processing MIG_rank
filter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      447.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1294.3  -394.2  -194.9    66.1  7278.2 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -3891.8813  3159.0137  -1.232   0.2195  
currentDF[, TRAIT]          117.8388    84.9705   1.387   0.1672  
Age                          18.7096    10.8310   1.727   0.0858 .
Gendermale                  245.6412   183.8225   1.336   0.1831  
ORdate_epoch                  0.1522     0.2125   0.717   0.4746  
Hypertension.compositeyes   111.6558   243.7087   0.458   0.6474  
DiabetesStatusDiabetes     -172.5838   199.3592  -0.866   0.3878  
SmokerStatusEx-smoker      -270.9610   178.3353  -1.519   0.1304  
SmokerStatusNever smoked   -355.5117   233.3597  -1.523   0.1294  
Med.Statin.LLDyes           174.7881   178.3630   0.980   0.3284  
Med.all.antiplateletyes     128.7351   280.3506   0.459   0.6466  
GFR_MDRD                     -0.3901     4.2122  -0.093   0.9263  
BMI                           6.2576    19.6713   0.318   0.7508  
MedHx_CVDNo                 207.1419   163.2691   1.269   0.2062  
stenose50-70%               328.0648  1331.6102   0.246   0.8057  
stenose70-90%               735.8283  1077.0176   0.683   0.4953  
stenose90-99%               547.5523  1074.5545   0.510   0.6110  
stenose100% (Occlusion)    2275.8845  1353.1786   1.682   0.0943 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1042 on 181 degrees of freedom
Multiple R-squared:  0.08024,   Adjusted R-squared:  -0.006141 
F-statistic: 0.9289 on 17 and 181 DF,  p-value: 0.5411

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MIG_rank 
Effect size...............: 117.8388 
Standard error............: 84.9705 
Odds ratio (effect size)..: 1.502235e+51 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.199542e+123 
T-value...................: 1.38682 
P-value...................: 0.1672022 
R^2.......................: 0.080245 
Adjusted r^2..............: -0.006141 
Sample size of AE DB......: 622 
Sample size of model......: 199 
Missing data %............: 68.00643 

- processing IP10_rank
filter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      454.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1401.9  -444.8  -187.2   128.7  7162.0 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -5133.6067  3191.5957  -1.608   0.1096  
currentDF[, TRAIT]          158.2081    85.4471   1.852   0.0659 .
Age                          19.4200    11.6620   1.665   0.0978 .
Gendermale                  314.9959   191.4456   1.645   0.1018  
ORdate_epoch                  0.2426     0.2100   1.155   0.2496  
Hypertension.compositeyes   143.3494   259.1648   0.553   0.5809  
DiabetesStatusDiabetes     -242.0118   217.1440  -1.115   0.2667  
SmokerStatusEx-smoker      -316.1903   193.2277  -1.636   0.1037  
SmokerStatusNever smoked   -383.7011   248.8845  -1.542   0.1251  
Med.Statin.LLDyes           185.2208   191.6020   0.967   0.3351  
Med.all.antiplateletyes     145.2881   296.7351   0.490   0.6251  
GFR_MDRD                     -1.3266     4.5560  -0.291   0.7713  
BMI                           8.1339    20.6342   0.394   0.6939  
MedHx_CVDNo                 200.3498   175.3821   1.142   0.2550  
stenose50-70%               310.1383  1364.5296   0.227   0.8205  
stenose70-90%               779.3861  1107.2050   0.704   0.4825  
stenose90-99%               566.3439  1100.9204   0.514   0.6076  
stenose100% (Occlusion)    2515.4602  1390.7539   1.809   0.0723 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1067 on 165 degrees of freedom
Multiple R-squared:    0.1, Adjusted R-squared:  0.00731 
F-statistic: 1.079 on 17 and 165 DF,  p-value: 0.3784

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: IP10_rank 
Effect size...............: 158.2081 
Standard error............: 85.44707 
Odds ratio (effect size)..: 5.11581e+68 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.772843e+141 
T-value...................: 1.851534 
P-value...................: 0.06588002 
R^2.......................: 0.100034 
Adjusted r^2..............: 0.00731 
Sample size of AE DB......: 622 
Sample size of model......: 183 
Missing data %............: 70.57878 

- processing Eotaxin1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      444.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1308.7  -368.3  -204.2    77.6  7468.7 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4.820e+03  3.068e+03  -1.571   0.1179  
currentDF[, TRAIT]         3.138e+01  7.868e+01   0.399   0.6905  
Age                        1.657e+01  1.066e+01   1.555   0.1217  
Gendermale                 2.676e+02  1.815e+02   1.474   0.1421  
ORdate_epoch               2.294e-01  2.039e-01   1.125   0.2621  
Hypertension.compositeyes  9.166e+01  2.355e+02   0.389   0.6975  
DiabetesStatusDiabetes    -1.884e+02  1.954e+02  -0.964   0.3362  
SmokerStatusEx-smoker     -2.437e+02  1.752e+02  -1.391   0.1660  
SmokerStatusNever smoked  -3.078e+02  2.298e+02  -1.339   0.1821  
Med.Statin.LLDyes          1.881e+02  1.753e+02   1.073   0.2846  
Med.all.antiplateletyes    1.274e+02  2.736e+02   0.466   0.6420  
GFR_MDRD                  -2.722e-02  4.181e+00  -0.007   0.9948  
BMI                        6.036e+00  1.957e+01   0.308   0.7582  
MedHx_CVDNo                2.003e+02  1.600e+02   1.252   0.2122  
stenose50-70%              4.756e+02  1.237e+03   0.384   0.7011  
stenose70-90%              7.893e+02  1.073e+03   0.736   0.4630  
stenose90-99%              6.272e+02  1.072e+03   0.585   0.5592  
stenose100% (Occlusion)    2.289e+03  1.346e+03   1.701   0.0907 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1039 on 184 degrees of freedom
Multiple R-squared:  0.07166,   Adjusted R-squared:  -0.01411 
F-statistic: 0.8355 on 17 and 184 DF,  p-value: 0.6507

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 31.37979 
Standard error............: 78.68213 
Odds ratio (effect size)..: 4.246892e+13 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.014661e+80 
T-value...................: 0.398817 
P-value...................: 0.6904903 
R^2.......................: 0.071658 
Adjusted r^2..............: -0.014112 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing TARC_rank
filter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      483.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1354.6  -454.7  -185.6   116.9  7127.9 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -6752.7641  3848.4579  -1.755   0.0812 .
currentDF[, TRAIT]          154.6719    92.1529   1.678   0.0952 .
Age                          23.4276    12.1875   1.922   0.0563 .
Gendermale                  346.7115   205.4402   1.688   0.0934 .
ORdate_epoch                  0.3257     0.2670   1.220   0.2243  
Hypertension.compositeyes   146.6537   258.9522   0.566   0.5720  
DiabetesStatusDiabetes     -176.8085   216.7827  -0.816   0.4159  
SmokerStatusEx-smoker      -265.6742   196.6755  -1.351   0.1787  
SmokerStatusNever smoked   -412.0578   251.3009  -1.640   0.1030  
Med.Statin.LLDyes           183.4846   204.5313   0.897   0.3710  
Med.all.antiplateletyes     173.4226   316.9885   0.547   0.5851  
GFR_MDRD                      1.2645     4.7466   0.266   0.7903  
BMI                           6.2787    21.9185   0.286   0.7749  
MedHx_CVDNo                 246.2081   180.5111   1.364   0.1745  
stenose50-70%               698.3228  1385.9959   0.504   0.6151  
stenose70-90%               879.8580  1132.4715   0.777   0.4383  
stenose90-99%               628.9701  1128.6828   0.557   0.5781  
stenose100% (Occlusion)    2475.0404  1437.2599   1.722   0.0870 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1095 on 160 degrees of freedom
Multiple R-squared:  0.09153,   Adjusted R-squared:  -0.004992 
F-statistic: 0.9483 on 17 and 160 DF,  p-value: 0.5191

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: TARC_rank 
Effect size...............: 154.6719 
Standard error............: 92.15287 
Odds ratio (effect size)..: 1.48989e+67 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.123442e+145 
T-value...................: 1.678427 
P-value...................: 0.09521576 
R^2.......................: 0.091533 
Adjusted r^2..............: -0.004992 
Sample size of AE DB......: 622 
Sample size of model......: 178 
Missing data %............: 71.38264 

- processing PARC_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      444.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1380.2  -396.4  -201.2   129.2  7325.8 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -6996.8391  3251.9937  -2.152   0.0327 *
currentDF[, TRAIT]          132.3850    82.7402   1.600   0.1113  
Age                          17.5095    10.5326   1.662   0.0981 .
Gendermale                  286.1963   179.0315   1.599   0.1116  
ORdate_epoch                  0.3791     0.2158   1.756   0.0807 .
Hypertension.compositeyes   150.5961   236.7820   0.636   0.5256  
DiabetesStatusDiabetes     -174.5672   193.9574  -0.900   0.3693  
SmokerStatusEx-smoker      -247.5996   173.3082  -1.429   0.1548  
SmokerStatusNever smoked   -348.6743   227.6578  -1.532   0.1273  
Med.Statin.LLDyes           179.7801   173.9886   1.033   0.3028  
Med.all.antiplateletyes     160.1420   271.8279   0.589   0.5565  
GFR_MDRD                      0.7968     4.1878   0.190   0.8493  
BMI                           8.9721    19.5380   0.459   0.6466  
MedHx_CVDNo                 187.3098   158.7399   1.180   0.2395  
stenose50-70%               507.3584  1226.4042   0.414   0.6796  
stenose70-90%               784.8871  1064.0447   0.738   0.4617  
stenose90-99%               653.6351  1060.0410   0.617   0.5383  
stenose100% (Occlusion)    2479.5238  1339.7343   1.851   0.0658 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1032 on 184 degrees of freedom
Multiple R-squared:  0.08361,   Adjusted R-squared:  -0.001061 
F-statistic: 0.9875 on 17 and 184 DF,  p-value: 0.4742

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: PARC_rank 
Effect size...............: 132.385 
Standard error............: 82.74019 
Odds ratio (effect size)..: 3.119336e+57 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.393316e+127 
T-value...................: 1.600008 
P-value...................: 0.1113124 
R^2.......................: 0.083606 
Adjusted r^2..............: -0.001061 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MDC_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      462.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1287.2  -391.7  -200.3    74.7  7440.2 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4896.9252  3270.6928  -1.497   0.1362  
currentDF[, TRAIT]           13.5914    85.9735   0.158   0.8746  
Age                          16.6546    11.2681   1.478   0.1412  
Gendermale                  294.9660   193.9929   1.520   0.1302  
ORdate_epoch                  0.2242     0.2183   1.027   0.3059  
Hypertension.compositeyes   132.5857   255.6573   0.519   0.6047  
DiabetesStatusDiabetes     -204.7261   212.6557  -0.963   0.3371  
SmokerStatusEx-smoker      -254.0823   188.0234  -1.351   0.1784  
SmokerStatusNever smoked   -337.9799   242.0809  -1.396   0.1645  
Med.Statin.LLDyes           203.1434   190.7612   1.065   0.2884  
Med.all.antiplateletyes     139.5250   317.4214   0.440   0.6608  
GFR_MDRD                      0.1919     4.3913   0.044   0.9652  
BMI                           6.7822    20.9799   0.323   0.7469  
MedHx_CVDNo                 226.8458   175.0531   1.296   0.1968  
stenose50-70%               453.8616  1374.0253   0.330   0.7416  
stenose70-90%               850.0781  1110.0615   0.766   0.4449  
stenose90-99%               660.1822  1106.2831   0.597   0.5515  
stenose100% (Occlusion)    2315.1944  1398.9814   1.655   0.0998 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1072 on 171 degrees of freedom
Multiple R-squared:  0.0745,    Adjusted R-squared:  -0.01751 
F-statistic: 0.8097 on 17 and 171 DF,  p-value: 0.6804

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MDC_rank 
Effect size...............: 13.59145 
Standard error............: 85.97347 
Odds ratio (effect size)..: 799264 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.215587e+79 
T-value...................: 0.158089 
P-value...................: 0.8745733 
R^2.......................: 0.074501 
Adjusted r^2..............: -0.017508 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing OPG_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             441.4               107.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1421.1  -392.9  -211.5   122.6  7237.8 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -5.402e+03  3.001e+03  -1.800   0.0735 .
currentDF[, TRAIT]         1.531e+02  7.505e+01   2.040   0.0428 *
Age                        1.964e+01  1.060e+01   1.853   0.0655 .
Gendermale                 2.542e+02  1.785e+02   1.424   0.1562  
ORdate_epoch               2.529e-01  1.981e-01   1.277   0.2033  
Hypertension.compositeyes  1.327e+02  2.338e+02   0.568   0.5710  
DiabetesStatusDiabetes    -1.840e+02  1.928e+02  -0.954   0.3412  
SmokerStatusEx-smoker     -2.865e+02  1.742e+02  -1.645   0.1017  
SmokerStatusNever smoked  -3.552e+02  2.261e+02  -1.571   0.1179  
Med.Statin.LLDyes          1.745e+02  1.733e+02   1.007   0.3153  
Med.all.antiplateletyes    8.236e+01  2.713e+02   0.304   0.7618  
GFR_MDRD                  -6.368e-02  4.135e+00  -0.015   0.9877  
BMI                        9.197e+00  1.942e+01   0.473   0.6365  
MedHx_CVDNo                1.945e+02  1.580e+02   1.231   0.2198  
stenose50-70%              4.457e+02  1.222e+03   0.365   0.7157  
stenose70-90%              8.461e+02  1.059e+03   0.799   0.4255  
stenose90-99%              6.701e+02  1.055e+03   0.635   0.5263  
stenose100% (Occlusion)    2.527e+03  1.334e+03   1.894   0.0597 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1027 on 184 degrees of freedom
Multiple R-squared:  0.0914,    Adjusted R-squared:  0.007451 
F-statistic: 1.089 on 17 and 184 DF,  p-value: 0.3675

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: OPG_rank 
Effect size...............: 153.0667 
Standard error............: 75.04838 
Odds ratio (effect size)..: 2.992356e+66 
Lower 95% CI..............: 392.234 
Upper 95% CI..............: 2.282873e+130 
T-value...................: 2.039573 
P-value...................: 0.04282259 
R^2.......................: 0.091397 
Adjusted r^2..............: 0.007451 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing sICAM1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             447.9              -120.7  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1291.4  -366.0  -189.4    76.1  7509.9 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4217.3615  3099.6176  -1.361   0.1753  
currentDF[, TRAIT]          -90.3037    79.5026  -1.136   0.2575  
Age                          13.5520    10.7416   1.262   0.2087  
Gendermale                  283.9378   179.6576   1.580   0.1157  
ORdate_epoch                  0.1871     0.2059   0.909   0.3647  
Hypertension.compositeyes    63.8956   236.0369   0.271   0.7869  
DiabetesStatusDiabetes     -210.8814   194.7792  -1.083   0.2804  
SmokerStatusEx-smoker      -228.6198   173.9032  -1.315   0.1903  
SmokerStatusNever smoked   -246.6294   229.1579  -1.076   0.2832  
Med.Statin.LLDyes           196.0482   174.4407   1.124   0.2625  
Med.all.antiplateletyes     144.7690   272.4283   0.531   0.5958  
GFR_MDRD                     -0.5267     4.1870  -0.126   0.9000  
BMI                           6.3807    19.5077   0.327   0.7440  
MedHx_CVDNo                 175.7011   160.2155   1.097   0.2742  
stenose50-70%               571.4894  1231.8463   0.464   0.6432  
stenose70-90%               933.0143  1072.3154   0.870   0.3854  
stenose90-99%               778.6167  1068.0066   0.729   0.4669  
stenose100% (Occlusion)    2400.4842  1342.4651   1.788   0.0754 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1035 on 184 degrees of freedom
Multiple R-squared:  0.07733,   Adjusted R-squared:  -0.007922 
F-statistic: 0.9071 on 17 and 184 DF,  p-value: 0.5665

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -90.3037 
Standard error............: 79.50263 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.854969e+28 
T-value...................: -1.135858 
P-value...................: 0.2574932 
R^2.......................: 0.077325 
Adjusted r^2..............: -0.007922 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing VEGFA_rank
filter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      437.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1421.0  -362.9  -206.0    90.3  7677.3 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4074.3774  3130.5063  -1.302   0.1950  
currentDF[, TRAIT]          -52.2011    83.8197  -0.623   0.5343  
Age                          11.6041    10.9702   1.058   0.2917  
Gendermale                  315.1773   185.9953   1.695   0.0921 .
ORdate_epoch                  0.2578     0.2327   1.108   0.2696  
Hypertension.compositeyes    23.6338   248.5109   0.095   0.9244  
DiabetesStatusDiabetes     -172.5580   199.5466  -0.865   0.3885  
SmokerStatusEx-smoker      -125.9425   177.4109  -0.710   0.4788  
SmokerStatusNever smoked   -231.4688   242.9764  -0.953   0.3422  
Med.Statin.LLDyes           160.3609   178.3420   0.899   0.3699  
Med.all.antiplateletyes     137.5293   266.3780   0.516   0.6064  
GFR_MDRD                     -0.5800     3.9431  -0.147   0.8833  
BMI                          -0.6112    20.4015  -0.030   0.9761  
MedHx_CVDNo                 131.0385   165.6383   0.791   0.4300  
stenose70-90%               277.0597   764.0882   0.363   0.7174  
stenose90-99%                38.9227   756.9654   0.051   0.9591  
stenose100% (Occlusion)    1596.5357  1077.6260   1.482   0.1404  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 996.2 on 160 degrees of freedom
Multiple R-squared:  0.06779,   Adjusted R-squared:  -0.02544 
F-statistic: 0.7271 on 16 and 160 DF,  p-value: 0.7634

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -52.20106 
Standard error............: 83.81972 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.766031e+48 
T-value...................: -0.622778 
P-value...................: 0.5343172 
R^2.......................: 0.067785 
Adjusted r^2..............: -0.025436 
Sample size of AE DB......: 622 
Sample size of model......: 177 
Missing data %............: 71.54341 

- processing TGFB_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      448.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1320.8  -365.1  -210.3    67.8  7495.4 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -4975.2103  3001.1090  -1.658   0.0991 .
currentDF[, TRAIT]          -14.4343    76.1781  -0.189   0.8499  
Age                          15.8442    10.6411   1.489   0.1382  
Gendermale                  277.3107   178.5411   1.553   0.1221  
ORdate_epoch                  0.2437     0.1993   1.223   0.2229  
Hypertension.compositeyes   102.7735   232.1093   0.443   0.6584  
DiabetesStatusDiabetes     -204.0086   189.2582  -1.078   0.2825  
SmokerStatusEx-smoker      -228.0073   173.8539  -1.311   0.1913  
SmokerStatusNever smoked   -284.8122   230.6085  -1.235   0.2184  
Med.Statin.LLDyes           199.2938   174.4554   1.142   0.2548  
Med.all.antiplateletyes     106.1065   280.4128   0.378   0.7056  
GFR_MDRD                     -0.1526     4.1605  -0.037   0.9708  
BMI                           5.0556    19.6857   0.257   0.7976  
MedHx_CVDNo                 183.7086   159.2307   1.154   0.2501  
stenose50-70%               551.1037  1239.6099   0.445   0.6571  
stenose70-90%               837.3984  1070.6262   0.782   0.4351  
stenose90-99%               688.1105  1067.0149   0.645   0.5198  
stenose100% (Occlusion)    2324.5216  1353.5328   1.717   0.0876 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1036 on 185 degrees of freedom
Multiple R-squared:  0.07193,   Adjusted R-squared:  -0.01335 
F-statistic: 0.8435 on 17 and 185 DF,  p-value: 0.6413

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: TGFB_rank 
Effect size...............: -14.43427 
Standard error............: 76.17809 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.761658e+58 
T-value...................: -0.189481 
P-value...................: 0.8499239 
R^2.......................: 0.071933 
Adjusted r^2..............: -0.013349 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing MMP2_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      407.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1519.7  -324.0  -171.5    38.6  7717.4 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -3007.7611  2803.7242  -1.073   0.2848  
currentDF[, TRAIT]           16.6203    71.5686   0.232   0.8166  
Age                           9.8691     9.5439   1.034   0.3025  
Gendermale                  229.0414   161.4428   1.419   0.1577  
ORdate_epoch                  0.1547     0.1851   0.836   0.4042  
Hypertension.compositeyes    58.0518   218.2743   0.266   0.7906  
DiabetesStatusDiabetes     -131.5848   174.7963  -0.753   0.4525  
SmokerStatusEx-smoker       -96.9790   158.1295  -0.613   0.5404  
SmokerStatusNever smoked   -144.6567   208.7915  -0.693   0.4893  
Med.Statin.LLDyes           125.1764   157.0460   0.797   0.4264  
Med.all.antiplateletyes     102.0896   246.5816   0.414   0.6793  
GFR_MDRD                     -1.2741     3.6585  -0.348   0.7280  
BMI                          -0.1049    18.1936  -0.006   0.9954  
MedHx_CVDNo                 107.2437   144.8341   0.740   0.4600  
stenose50-70%               388.8390  1116.3836   0.348   0.7280  
stenose70-90%               659.4346   969.1413   0.680   0.4971  
stenose90-99%               477.6694   964.9734   0.495   0.6212  
stenose100% (Occlusion)    2071.4666  1217.4230   1.702   0.0905 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 938.2 on 184 degrees of freedom
Multiple R-squared:  0.05599,   Adjusted R-squared:  -0.03123 
F-statistic: 0.6419 on 17 and 184 DF,  p-value: 0.8551

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MMP2_rank 
Effect size...............: 16.62032 
Standard error............: 71.56859 
Odds ratio (effect size)..: 16523898 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.37571e+68 
T-value...................: 0.232229 
P-value...................: 0.8166181 
R^2.......................: 0.055986 
Adjusted r^2..............: -0.031233 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP8_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      407.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1476.7  -336.8  -171.6    34.8  7698.1 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -3093.0254  2754.3117  -1.123    0.263  
currentDF[, TRAIT]          -59.8434    69.0026  -0.867    0.387  
Age                           9.1088     9.4974   0.959    0.339  
Gendermale                  248.5477   162.2467   1.532    0.127  
ORdate_epoch                  0.1534     0.1818   0.844    0.400  
Hypertension.compositeyes    50.4339   215.0734   0.234    0.815  
DiabetesStatusDiabetes     -149.7964   175.7979  -0.852    0.395  
SmokerStatusEx-smoker      -102.0318   157.6322  -0.647    0.518  
SmokerStatusNever smoked   -139.2726   206.5284  -0.674    0.501  
Med.Statin.LLDyes           123.9846   156.7426   0.791    0.430  
Med.all.antiplateletyes      95.1360   245.9113   0.387    0.699  
GFR_MDRD                     -1.8758     3.6648  -0.512    0.609  
BMI                           0.9094    18.1763   0.050    0.960  
MedHx_CVDNo                  93.8097   145.2186   0.646    0.519  
stenose50-70%               528.8410  1122.2883   0.471    0.638  
stenose70-90%               849.7929   989.7314   0.859    0.392  
stenose90-99%               648.4726   981.7045   0.661    0.510  
stenose100% (Occlusion)    2270.9309  1236.8623   1.836    0.068 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 936.4 on 184 degrees of freedom
Multiple R-squared:  0.05955,   Adjusted R-squared:  -0.02734 
F-statistic: 0.6854 on 17 and 184 DF,  p-value: 0.8149

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MMP8_rank 
Effect size...............: -59.8434 
Standard error............: 69.0026 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.578764e+32 
T-value...................: -0.867263 
P-value...................: 0.3869275 
R^2.......................: 0.059553 
Adjusted r^2..............: -0.027336 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP9_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      407.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1519.2  -327.7  -167.5    53.9  7726.8 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -2930.9426  2769.2601  -1.058   0.2913  
currentDF[, TRAIT]           10.5011    68.2809   0.154   0.8779  
Age                           9.7963     9.5464   1.026   0.3062  
Gendermale                  222.5818   160.4802   1.387   0.1671  
ORdate_epoch                  0.1504     0.1834   0.820   0.4133  
Hypertension.compositeyes    55.6755   218.6710   0.255   0.7993  
DiabetesStatusDiabetes     -127.4860   176.1701  -0.724   0.4702  
SmokerStatusEx-smoker       -94.8230   157.7383  -0.601   0.5485  
SmokerStatusNever smoked   -140.5054   207.4883  -0.677   0.4991  
Med.Statin.LLDyes           123.9044   157.1542   0.788   0.4315  
Med.all.antiplateletyes     103.3401   246.5994   0.419   0.6757  
GFR_MDRD                     -1.2840     3.6862  -0.348   0.7280  
BMI                          -0.5338    18.1677  -0.029   0.9766  
MedHx_CVDNo                 108.2155   145.3247   0.745   0.4574  
stenose50-70%               399.0087  1115.1733   0.358   0.7209  
stenose70-90%               659.1119   969.8995   0.680   0.4976  
stenose90-99%               476.5197   965.6405   0.493   0.6223  
stenose100% (Occlusion)    2068.3609  1217.6104   1.699   0.0911 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 938.3 on 184 degrees of freedom
Multiple R-squared:  0.05583,   Adjusted R-squared:  -0.0314 
F-statistic:  0.64 on 17 and 184 DF,  p-value: 0.8568

Analyzing in dataset ' AEDB.CEA ' the association of ' COL4A2 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: COL4A2 
Trait/outcome.............: MMP9_rank 
Effect size...............: 10.50105 
Standard error............: 68.28088 
Odds ratio (effect size)..: 36353.85 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.812974e+62 
T-value...................: 0.153792 
P-value...................: 0.8779423 
R^2.......................: 0.055831 
Adjusted r^2..............: -0.031403 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

Analysis of LDLR.

- processing IL2_rank
filter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus, 
    data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]  DiabetesStatusDiabetes  
                253.40                  -39.59                  -97.89  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-328.09 -149.83  -58.81   78.25 3029.61 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                520.91268 1093.38784   0.476    0.634
currentDF[, TRAIT]         -46.31053   28.25716  -1.639    0.103
Age                         -5.10282    3.77433  -1.352    0.178
Gendermale                  23.38151   67.89886   0.344    0.731
ORdate_epoch                 0.03022    0.07798   0.388    0.699
Hypertension.compositeyes   60.97765   88.40359   0.690    0.491
DiabetesStatusDiabetes    -110.81618   74.07574  -1.496    0.137
SmokerStatusEx-smoker       30.02386   63.48378   0.473    0.637
SmokerStatusNever smoked   -39.51239   86.43680  -0.457    0.648
Med.Statin.LLDyes          -91.49241   62.36361  -1.467    0.145
Med.all.antiplateletyes     -2.82203   98.82217  -0.029    0.977
GFR_MDRD                    -1.38985    1.62177  -0.857    0.393
BMI                         -8.84295    8.41422  -1.051    0.295
MedHx_CVDNo                -25.11557   59.00172  -0.426    0.671
stenose70-90%               14.95581  252.45003   0.059    0.953
stenose90-99%               44.26366  250.83339   0.176    0.860
stenose100% (Occlusion)    -82.85074  365.34379  -0.227    0.821

Residual standard error: 336.7 on 146 degrees of freedom
Multiple R-squared:  0.06159,   Adjusted R-squared:  -0.04125 
F-statistic: 0.5989 on 16 and 146 DF,  p-value: 0.8809

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL2_rank 
Effect size...............: -46.31053 
Standard error............: 28.25716 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8721.072 
T-value...................: -1.638896 
P-value...................: 0.1033881 
R^2.......................: 0.061591 
Adjusted r^2..............: -0.041248 
Sample size of AE DB......: 622 
Sample size of model......: 163 
Missing data %............: 73.79421 

- processing IL4_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      238.4  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-318.43 -156.42  -72.46   60.35 3030.71 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                 62.83957 1270.88420   0.049    0.961
currentDF[, TRAIT]         -28.34536   32.81173  -0.864    0.389
Age                         -5.27680    4.30053  -1.227    0.222
Gendermale                  37.76020   76.81928   0.492    0.624
ORdate_epoch                 0.06565    0.09117   0.720    0.473
Hypertension.compositeyes   83.53592  100.90231   0.828    0.409
DiabetesStatusDiabetes    -108.26611   83.93141  -1.290    0.199
SmokerStatusEx-smoker       10.19777   70.01742   0.146    0.884
SmokerStatusNever smoked   -27.76318   99.49359  -0.279    0.781
Med.Statin.LLDyes         -119.10325   73.43785  -1.622    0.107
Med.all.antiplateletyes     14.13995  110.22870   0.128    0.898
GFR_MDRD                    -1.51017    2.00257  -0.754    0.452
BMI                         -8.09419   10.02896  -0.807    0.421
MedHx_CVDNo                -38.69057   66.81842  -0.579    0.564
stenose70-90%               27.90398  271.83240   0.103    0.918
stenose90-99%               45.64903  269.66032   0.169    0.866
stenose100% (Occlusion)    -57.54530  394.00776  -0.146    0.884

Residual standard error: 358.2 on 128 degrees of freedom
Multiple R-squared:  0.05576,   Adjusted R-squared:  -0.06227 
F-statistic: 0.4724 on 16 and 128 DF,  p-value: 0.9562

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL4_rank 
Effect size...............: -28.34536 
Standard error............: 32.81173 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4.165559e+15 
T-value...................: -0.863879 
P-value...................: 0.3892703 
R^2.......................: 0.055762 
Adjusted r^2..............: -0.062268 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing IL5_rank
filter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            234.19              -49.58  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-374.28 -152.05  -54.11   58.50 3018.55 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                493.67791 1131.34306   0.436   0.6632  
currentDF[, TRAIT]         -62.20168   28.84504  -2.156   0.0327 *
Age                         -5.56773    3.99237  -1.395   0.1653  
Gendermale                  32.71229   69.62715   0.470   0.6392  
ORdate_epoch                 0.02714    0.08253   0.329   0.7427  
Hypertension.compositeyes   72.34639   92.26184   0.784   0.4343  
DiabetesStatusDiabetes    -102.47357   74.99751  -1.366   0.1740  
SmokerStatusEx-smoker       11.82359   63.42663   0.186   0.8524  
SmokerStatusNever smoked   -36.60120   91.59944  -0.400   0.6901  
Med.Statin.LLDyes          -96.29147   65.25316  -1.476   0.1423  
Med.all.antiplateletyes     -2.00327  101.07822  -0.020   0.9842  
GFR_MDRD                    -0.65153    1.73332  -0.376   0.7076  
BMI                         -7.83577    8.60479  -0.911   0.3640  
MedHx_CVDNo                -27.95813   61.54952  -0.454   0.6504  
stenose70-90%               29.28637  213.04120   0.137   0.8909  
stenose90-99%               50.26398  210.43995   0.239   0.8116  
stenose100% (Occlusion)   -112.98204  340.10373  -0.332   0.7402  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 340.8 on 141 degrees of freedom
Multiple R-squared:  0.0656,    Adjusted R-squared:  -0.04044 
F-statistic: 0.6186 on 16 and 141 DF,  p-value: 0.8649

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL5_rank 
Effect size...............: -62.20168 
Standard error............: 28.84504 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 0.003 
T-value...................: -2.156408 
P-value...................: 0.03274785 
R^2.......................: 0.065596 
Adjusted r^2..............: -0.040435 
Sample size of AE DB......: 622 
Sample size of model......: 158 
Missing data %............: 74.59807 

- processing IL6_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
             235.2               -47.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-373.36 -143.85  -60.65   60.68 3035.36 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -248.62021 1140.08716  -0.218    0.828  
currentDF[, TRAIT]         -54.74029   27.32206  -2.004    0.047 *
Age                         -4.85318    3.99357  -1.215    0.226  
Gendermale                  13.81295   65.72550   0.210    0.834  
ORdate_epoch                 0.07104    0.07596   0.935    0.351  
Hypertension.compositeyes   62.84548   87.81658   0.716    0.475  
DiabetesStatusDiabetes     -82.96563   73.47596  -1.129    0.261  
SmokerStatusEx-smoker       18.25252   63.03640   0.290    0.773  
SmokerStatusNever smoked   -39.70686   87.45826  -0.454    0.650  
Med.Statin.LLDyes          -86.93234   62.13102  -1.399    0.164  
Med.all.antiplateletyes    -22.47669  100.75498  -0.223    0.824  
GFR_MDRD                    -1.15257    1.69811  -0.679    0.498  
BMI                         -3.58069    7.28361  -0.492    0.624  
MedHx_CVDNo                -26.49345   59.23920  -0.447    0.655  
stenose50-70%              100.22164  405.28156   0.247    0.805  
stenose70-90%              120.51754  352.11232   0.342    0.733  
stenose90-99%              149.41896  351.46697   0.425    0.671  
stenose100% (Occlusion)     68.29886  443.67050   0.154    0.878  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 337.9 on 146 degrees of freedom
Multiple R-squared:  0.05976,   Adjusted R-squared:  -0.04972 
F-statistic: 0.5458 on 17 and 146 DF,  p-value: 0.925

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL6_rank 
Effect size...............: -54.74029 
Standard error............: 27.32206 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 0.305 
T-value...................: -2.00352 
P-value...................: 0.04697195 
R^2.......................: 0.059756 
Adjusted r^2..............: -0.049725 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL8_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      245.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-348.09 -149.23  -61.71   61.30 3070.28 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                235.68769 1251.63975   0.188    0.851
currentDF[, TRAIT]          39.24104   30.24855   1.297    0.197
Age                         -3.99713    4.09374  -0.976    0.331
Gendermale                   6.68264   73.09637   0.091    0.927
ORdate_epoch                 0.03254    0.08331   0.391    0.697
Hypertension.compositeyes   52.22622   90.67071   0.576    0.566
DiabetesStatusDiabetes     -96.31304   76.80799  -1.254    0.212
SmokerStatusEx-smoker       22.74462   68.62257   0.331    0.741
SmokerStatusNever smoked   -41.33647   98.76103  -0.419    0.676
Med.Statin.LLDyes          -78.89004   65.71581  -1.200    0.232
Med.all.antiplateletyes     25.72044  101.24727   0.254    0.800
GFR_MDRD                    -2.21278    1.61926  -1.367    0.174
BMI                         -2.66139    7.73697  -0.344    0.731
MedHx_CVDNo                -15.35188   64.05711  -0.240    0.811
stenose50-70%              112.97386  419.48461   0.269    0.788
stenose70-90%               69.64010  366.34889   0.190    0.850
stenose90-99%              113.71150  365.01635   0.312    0.756
stenose100% (Occlusion)    176.37355  521.12338   0.338    0.736

Residual standard error: 350.2 on 136 degrees of freedom
Multiple R-squared:  0.05275,   Adjusted R-squared:  -0.06566 
F-statistic: 0.4455 on 17 and 136 DF,  p-value: 0.9712

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL8_rank 
Effect size...............: 39.24104 
Standard error............: 30.24855 
Odds ratio (effect size)..: 1.101967e+17 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.16955e+42 
T-value...................: 1.297287 
P-value...................: 0.1967289 
R^2.......................: 0.052748 
Adjusted r^2..............: -0.065659 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing IL9_rank
filter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            238.02               47.24  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-316.07 -136.86  -52.12   51.94 3054.92 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -217.20901  960.87559  -0.226   0.8214  
currentDF[, TRAIT]          47.03353   24.19720   1.944   0.0536 .
Age                         -2.53338    3.52165  -0.719   0.4729  
Gendermale                  54.23655   58.37864   0.929   0.3542  
ORdate_epoch                 0.05125    0.06295   0.814   0.4167  
Hypertension.compositeyes   88.41573   78.82601   1.122   0.2636  
DiabetesStatusDiabetes     -69.21586   65.24616  -1.061   0.2903  
SmokerStatusEx-smoker      -21.10761   57.03112  -0.370   0.7118  
SmokerStatusNever smoked   -71.42706   72.62220  -0.984   0.3268  
Med.Statin.LLDyes          -50.52447   57.66801  -0.876   0.3822  
Med.all.antiplateletyes     13.28236   95.33755   0.139   0.8894  
GFR_MDRD                    -1.58419    1.32668  -1.194   0.2341  
BMI                         -2.77936    6.42391  -0.433   0.6658  
MedHx_CVDNo                -14.04654   52.71192  -0.266   0.7902  
stenose50-70%               43.29069  410.25876   0.106   0.9161  
stenose70-90%              110.93439  332.46474   0.334   0.7390  
stenose90-99%              114.07784  331.06438   0.345   0.7308  
stenose100% (Occlusion)    134.42324  421.12264   0.319   0.7500  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 321.6 on 168 degrees of freedom
Multiple R-squared:  0.06134,   Adjusted R-squared:  -0.03364 
F-statistic: 0.6458 on 17 and 168 DF,  p-value: 0.8512

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL9_rank 
Effect size...............: 47.03353 
Standard error............: 24.1972 
Odds ratio (effect size)..: 2.669337e+20 
Lower 95% CI..............: 0.675 
Upper 95% CI..............: 1.055548e+41 
T-value...................: 1.943759 
P-value...................: 0.05359534 
R^2.......................: 0.061339 
Adjusted r^2..............: -0.033645 
Sample size of AE DB......: 622 
Sample size of model......: 186 
Missing data %............: 70.09646 

- processing IL10_rank
filter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
        236  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-405.59 -157.26  -73.03   59.17 3051.99 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                387.18086 1392.05420   0.278    0.781
currentDF[, TRAIT]         -31.55853   33.79919  -0.934    0.352
Age                         -4.59064    4.54165  -1.011    0.314
Gendermale                  34.47565   80.04331   0.431    0.667
ORdate_epoch                 0.03035    0.09741   0.312    0.756
Hypertension.compositeyes   73.07362  105.82658   0.691    0.491
DiabetesStatusDiabetes    -109.45375   87.42213  -1.252    0.213
SmokerStatusEx-smoker        6.96094   73.89260   0.094    0.925
SmokerStatusNever smoked   -16.15678  102.83348  -0.157    0.875
Med.Statin.LLDyes         -100.60239   73.69258  -1.365    0.175
Med.all.antiplateletyes      7.48007  114.56175   0.065    0.948
GFR_MDRD                    -1.07520    2.10036  -0.512    0.610
BMI                         -6.67115   10.37607  -0.643    0.521
MedHx_CVDNo                -38.79323   72.49249  -0.535    0.594
stenose70-90%               18.34613  279.60763   0.066    0.948
stenose90-99%               42.19636  277.37485   0.152    0.879
stenose100% (Occlusion)    -71.78452  405.70190  -0.177    0.860

Residual standard error: 367.3 on 122 degrees of freedom
Multiple R-squared:  0.04846,   Adjusted R-squared:  -0.07633 
F-statistic: 0.3884 on 16 and 122 DF,  p-value: 0.9831

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL10_rank 
Effect size...............: -31.55853 
Standard error............: 33.79919 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.160802e+15 
T-value...................: -0.933707 
P-value...................: 0.3522996 
R^2.......................: 0.048464 
Adjusted r^2..............: -0.076328 
Sample size of AE DB......: 622 
Sample size of model......: 139 
Missing data %............: 77.65273 

- processing IL12_rank
filter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-385.06 -156.12  -62.14   62.01 3003.89 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                472.05050 1310.13762   0.360   0.7192  
currentDF[, TRAIT]         -39.64004   31.87273  -1.244   0.2159  
Age                         -5.57318    4.39702  -1.267   0.2073  
Gendermale                  36.59613   75.38136   0.485   0.6282  
ORdate_epoch                 0.03843    0.09123   0.421   0.6743  
Hypertension.compositeyes   59.04102  102.52622   0.576   0.5657  
DiabetesStatusDiabetes    -117.45838   81.68289  -1.438   0.1529  
SmokerStatusEx-smoker       10.44556   72.49122   0.144   0.8857  
SmokerStatusNever smoked   -44.12572   96.18877  -0.459   0.6472  
Med.Statin.LLDyes         -121.08550   72.15387  -1.678   0.0957 .
Med.all.antiplateletyes    -12.09649  109.66328  -0.110   0.9123  
GFR_MDRD                    -1.23103    1.94082  -0.634   0.5270  
BMI                         -8.80030    9.82068  -0.896   0.3719  
MedHx_CVDNo                -50.37161   66.15698  -0.761   0.4478  
stenose70-90%               38.41980  268.53278   0.143   0.8865  
stenose90-99%               46.61311  267.45991   0.174   0.8619  
stenose100% (Occlusion)     36.25866  470.51257   0.077   0.9387  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 354.6 on 129 degrees of freedom
Multiple R-squared:  0.0616,    Adjusted R-squared:  -0.05479 
F-statistic: 0.5293 on 16 and 129 DF,  p-value: 0.9275

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL12_rank 
Effect size...............: -39.64004 
Standard error............: 31.87273 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8225467033 
T-value...................: -1.243698 
P-value...................: 0.2158661 
R^2.......................: 0.061604 
Adjusted r^2..............: -0.054786 
Sample size of AE DB......: 622 
Sample size of model......: 146 
Missing data %............: 76.52733 

- processing IL13_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-316.86 -145.58  -53.09   48.01 3072.98 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)
(Intercept)               -66.75464  969.38841  -0.069    0.945
currentDF[, TRAIT]         35.26933   24.97266   1.412    0.160
Age                        -0.66512    3.44738  -0.193    0.847
Gendermale                 24.16385   57.82098   0.418    0.677
ORdate_epoch                0.04291    0.06431   0.667    0.505
Hypertension.compositeyes  37.60088   75.50477   0.498    0.619
DiabetesStatusDiabetes    -77.13837   62.50994  -1.234    0.219
SmokerStatusEx-smoker     -43.33149   56.39738  -0.768    0.443
SmokerStatusNever smoked  -96.54963   73.81055  -1.308    0.192
Med.Statin.LLDyes         -64.45092   56.07011  -1.149    0.252
Med.all.antiplateletyes   -76.90992   87.31003  -0.881    0.380
GFR_MDRD                   -2.03679    1.33658  -1.524    0.129
BMI                        -0.66104    6.25617  -0.106    0.916
MedHx_CVDNo                11.65805   51.11402   0.228    0.820
stenose50-70%              57.08558  396.22282   0.144    0.886
stenose70-90%              94.29734  343.01973   0.275    0.784
stenose90-99%              80.68105  342.63429   0.235    0.814
stenose100% (Occlusion)    25.83165  429.55337   0.060    0.952

Residual standard error: 331.8 on 184 degrees of freedom
Multiple R-squared:  0.04798,   Adjusted R-squared:  -0.03998 
F-statistic: 0.5454 on 17 and 184 DF,  p-value: 0.9265

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL13_rank 
Effect size...............: 35.26933 
Standard error............: 24.97266 
Odds ratio (effect size)..: 2.076235e+15 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.753488e+36 
T-value...................: 1.412318 
P-value...................: 0.1595455 
R^2.......................: 0.047976 
Adjusted r^2..............: -0.039983 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing IL21_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]            GFR_MDRD  
            360.26               31.85               -1.70  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-331.19 -149.43  -55.93   33.13 3070.69 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)
(Intercept)               -51.17276  968.62788  -0.053    0.958
currentDF[, TRAIT]         38.36944   24.74939   1.550    0.123
Age                        -0.82856    3.41295  -0.243    0.808
Gendermale                 21.85230   57.86319   0.378    0.706
ORdate_epoch                0.04402    0.06411   0.687    0.493
Hypertension.compositeyes  34.00657   75.23164   0.452    0.652
DiabetesStatusDiabetes    -78.03874   62.35196  -1.252    0.212
SmokerStatusEx-smoker     -40.68773   55.99664  -0.727    0.468
SmokerStatusNever smoked  -95.90473   73.40625  -1.306    0.193
Med.Statin.LLDyes         -66.58186   56.09475  -1.187    0.237
Med.all.antiplateletyes   -79.26136   87.26664  -0.908    0.365
GFR_MDRD                   -2.10049    1.33412  -1.574    0.117
BMI                        -0.86179    6.24405  -0.138    0.890
MedHx_CVDNo                11.20776   51.02617   0.220    0.826
stenose50-70%              60.01803  395.32151   0.152    0.879
stenose70-90%              94.64212  342.47844   0.276    0.783
stenose90-99%              78.86208  342.07516   0.231    0.818
stenose100% (Occlusion)    12.96177  429.15073   0.030    0.976

Residual standard error: 331.5 on 184 degrees of freedom
Multiple R-squared:  0.05006,   Adjusted R-squared:  -0.0377 
F-statistic: 0.5704 on 17 and 184 DF,  p-value: 0.9106

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IL21_rank 
Effect size...............: 38.36944 
Standard error............: 24.74939 
Odds ratio (effect size)..: 4.609299e+16 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.379461e+37 
T-value...................: 1.550319 
P-value...................: 0.1227833 
R^2.......................: 0.050064 
Adjusted r^2..............: -0.037702 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing INFG_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      243.6  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-355.24 -147.60  -70.37   75.36 3069.14 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)                132.49024 1257.86295   0.105    0.916
currentDF[, TRAIT]         -22.33873   32.56829  -0.686    0.494
Age                         -5.38684    4.14607  -1.299    0.196
Gendermale                  22.44825   75.67250   0.297    0.767
ORdate_epoch                 0.04762    0.08509   0.560    0.577
Hypertension.compositeyes   41.22299  100.22874   0.411    0.682
DiabetesStatusDiabetes    -115.54334   76.11146  -1.518    0.131
SmokerStatusEx-smoker       29.12536   68.55121   0.425    0.672
SmokerStatusNever smoked   -19.37587   93.19090  -0.208    0.836
Med.Statin.LLDyes         -104.06856   67.76233  -1.536    0.127
Med.all.antiplateletyes     21.19570   99.32193   0.213    0.831
GFR_MDRD                    -1.70420    1.67524  -1.017    0.311
BMI                         -4.45845    7.73944  -0.576    0.566
MedHx_CVDNo                -38.27717   65.33247  -0.586    0.559
stenose50-70%              119.69398  419.42136   0.285    0.776
stenose70-90%              133.62269  365.91206   0.365    0.716
stenose90-99%              151.11773  363.88144   0.415    0.679
stenose100% (Occlusion)    200.51545  517.13241   0.388    0.699

Residual standard error: 349.1 on 136 degrees of freedom
Multiple R-squared:  0.05155,   Adjusted R-squared:  -0.06701 
F-statistic: 0.4348 on 17 and 136 DF,  p-value: 0.9746

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: INFG_rank 
Effect size...............: -22.33873 
Standard error............: 32.56829 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.049788e+18 
T-value...................: -0.685904 
P-value...................: 0.493941 
R^2.......................: 0.051545 
Adjusted r^2..............: -0.067011 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing TNFA_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
        239  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-365.27 -159.80  -66.71   64.14 2997.30 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                576.76393 1272.02456   0.453   0.6510  
currentDF[, TRAIT]         -47.42767   31.98864  -1.483   0.1406  
Age                         -5.21971    4.32130  -1.208   0.2293  
Gendermale                  24.55641   75.48357   0.325   0.7455  
ORdate_epoch                 0.02532    0.08864   0.286   0.7756  
Hypertension.compositeyes   78.30478  101.16598   0.774   0.4403  
DiabetesStatusDiabetes    -107.85337   81.68760  -1.320   0.1891  
SmokerStatusEx-smoker       28.25752   70.42564   0.401   0.6889  
SmokerStatusNever smoked   -36.42460   97.54724  -0.373   0.7095  
Med.Statin.LLDyes         -122.13493   72.55619  -1.683   0.0948 .
Med.all.antiplateletyes     -6.60513  108.89571  -0.061   0.9517  
GFR_MDRD                    -1.12867    1.87635  -0.602   0.5486  
BMI                         -8.74042    9.29258  -0.941   0.3487  
MedHx_CVDNo                -62.36279   68.29523  -0.913   0.3629  
stenose70-90%               34.50883  269.79503   0.128   0.8984  
stenose90-99%               53.76952  267.05490   0.201   0.8408  
stenose100% (Occlusion)     70.70280  470.54635   0.150   0.8808  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 356.7 on 128 degrees of freedom
Multiple R-squared:  0.06219,   Adjusted R-squared:  -0.05503 
F-statistic: 0.5305 on 16 and 128 DF,  p-value: 0.9267

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: TNFA_rank 
Effect size...............: -47.42767 
Standard error............: 31.98864 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4282526 
T-value...................: -1.482641 
P-value...................: 0.1406276 
R^2.......................: 0.062191 
Adjusted r^2..............: -0.055035 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing MIF_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-359.92 -146.45  -59.04   46.75 3056.28 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -757.99845 1048.40701  -0.723    0.471
currentDF[, TRAIT]          42.58446   28.70174   1.484    0.140
Age                         -1.25244    3.38313  -0.370    0.712
Gendermale                  27.93648   57.58191   0.485    0.628
ORdate_epoch                 0.10075    0.07174   1.404    0.162
Hypertension.compositeyes   41.59254   75.70297   0.549    0.583
DiabetesStatusDiabetes     -79.12648   62.34892  -1.269    0.206
SmokerStatusEx-smoker      -24.79177   55.80427  -0.444    0.657
SmokerStatusNever smoked   -86.25570   72.62906  -1.188    0.237
Med.Statin.LLDyes          -62.25468   55.93172  -1.113    0.267
Med.all.antiplateletyes    -83.62314   87.52348  -0.955    0.341
GFR_MDRD                    -1.76925    1.35443  -1.306    0.193
BMI                         -1.34071    6.24865  -0.215    0.830
MedHx_CVDNo                 11.86909   51.08519   0.232    0.817
stenose50-70%               68.43377  395.20937   0.173    0.863
stenose70-90%               85.04846  343.23139   0.248    0.805
stenose90-99%               77.08834  342.52342   0.225    0.822
stenose100% (Occlusion)     -5.89327  429.81606  -0.014    0.989

Residual standard error: 331.6 on 184 degrees of freedom
Multiple R-squared:  0.04903,   Adjusted R-squared:  -0.03883 
F-statistic: 0.5581 on 17 and 184 DF,  p-value: 0.9187

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MIF_rank 
Effect size...............: 42.58446 
Standard error............: 28.70174 
Odds ratio (effect size)..: 3.120309e+18 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.425809e+42 
T-value...................: 1.483689 
P-value...................: 0.1396023 
R^2.......................: 0.049033 
Adjusted r^2..............: -0.038828 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MCP1_rank
filter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      243.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-381.53 -140.95  -54.62   38.91 3090.37 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -500.71499  998.17747  -0.502    0.617
currentDF[, TRAIT]          35.91094   24.57144   1.461    0.146
Age                         -0.64547    3.43022  -0.188    0.851
Gendermale                  21.68959   58.49053   0.371    0.711
ORdate_epoch                 0.07555    0.06606   1.144    0.254
Hypertension.compositeyes   39.21185   76.06512   0.516    0.607
DiabetesStatusDiabetes     -80.54276   62.74837  -1.284    0.201
SmokerStatusEx-smoker      -33.92924   56.00120  -0.606    0.545
SmokerStatusNever smoked   -92.78949   73.34844  -1.265    0.207
Med.Statin.LLDyes          -57.09875   56.37604  -1.013    0.312
Med.all.antiplateletyes   -110.19940   92.87553  -1.187    0.237
GFR_MDRD                    -2.15154    1.34509  -1.600    0.111
BMI                          0.19588    6.36651   0.031    0.975
MedHx_CVDNo                  7.33760   51.50032   0.142    0.887
stenose50-70%              107.50288  395.33754   0.272    0.786
stenose70-90%              117.22661  342.97396   0.342    0.733
stenose90-99%              122.27141  341.77476   0.358    0.721
stenose100% (Occlusion)     24.83669  431.84308   0.058    0.954

Residual standard error: 332.5 on 182 degrees of freedom
Multiple R-squared:  0.05254,   Adjusted R-squared:  -0.03595 
F-statistic: 0.5937 on 17 and 182 DF,  p-value: 0.894

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MCP1_rank 
Effect size...............: 35.91094 
Standard error............: 24.57144 
Odds ratio (effect size)..: 3.943889e+15 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.247574e+36 
T-value...................: 1.461491 
P-value...................: 0.1456051 
R^2.......................: 0.052544 
Adjusted r^2..............: -0.035955 
Sample size of AE DB......: 622 
Sample size of model......: 200 
Missing data %............: 67.84566 

- processing MIP1a_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            235.78               41.82  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-341.81 -132.95  -52.18   47.79 3091.88 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)
(Intercept)               -50.02159  949.24544  -0.053    0.958
currentDF[, TRAIT]         38.12778   24.37065   1.564    0.120
Age                        -2.72637    3.37808  -0.807    0.421
Gendermale                 50.40994   58.36560   0.864    0.389
ORdate_epoch                0.04184    0.06264   0.668    0.505
Hypertension.compositeyes  84.74853   76.45651   1.108    0.269
DiabetesStatusDiabetes    -73.66856   63.03109  -1.169    0.244
SmokerStatusEx-smoker     -17.17287   56.48504  -0.304    0.761
SmokerStatusNever smoked  -69.97761   72.57658  -0.964    0.336
Med.Statin.LLDyes         -46.25944   56.38082  -0.820    0.413
Med.all.antiplateletyes     1.12804   94.64381   0.012    0.991
GFR_MDRD                   -1.54156    1.31782  -1.170    0.244
BMI                        -2.56959    6.24055  -0.412    0.681
MedHx_CVDNo               -11.37609   52.33424  -0.217    0.828
stenose50-70%              32.01360  410.43805   0.078    0.938
stenose70-90%              77.67143  332.35743   0.234    0.815
stenose90-99%              79.13719  331.81675   0.238    0.812
stenose100% (Occlusion)    61.23816  418.48362   0.146    0.884

Residual standard error: 320.4 on 171 degrees of freedom
Multiple R-squared:  0.05298,   Adjusted R-squared:  -0.04117 
F-statistic: 0.5628 on 17 and 171 DF,  p-value: 0.9153

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 38.12778 
Standard error............: 24.37065 
Odds ratio (effect size)..: 3.619789e+16 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.010937e+37 
T-value...................: 1.564496 
P-value...................: 0.1195499 
R^2.......................: 0.052982 
Adjusted r^2..............: -0.041166 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing RANTES_rank
filter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      242.5  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-350.40 -148.30  -66.91   36.80 3121.71 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -398.37766 1048.07446  -0.380    0.704
currentDF[, TRAIT]          20.40994   27.49949   0.742    0.459
Age                         -1.14433    3.50000  -0.327    0.744
Gendermale                  26.16119   59.88022   0.437    0.663
ORdate_epoch                 0.07151    0.07066   1.012    0.313
Hypertension.compositeyes   34.60088   79.41462   0.436    0.664
DiabetesStatusDiabetes     -75.79693   64.74275  -1.171    0.243
SmokerStatusEx-smoker      -27.09380   57.76591  -0.469    0.640
SmokerStatusNever smoked   -81.51674   74.70162  -1.091    0.277
Med.Statin.LLDyes          -57.82350   57.31030  -1.009    0.314
Med.all.antiplateletyes    -80.35812   93.48740  -0.860    0.391
GFR_MDRD                    -2.02034    1.36618  -1.479    0.141
BMI                         -1.66582    6.45305  -0.258    0.797
MedHx_CVDNo                 14.33648   53.58532   0.268    0.789
stenose50-70%              106.76195  427.57292   0.250    0.803
stenose70-90%              105.57491  348.81314   0.303    0.762
stenose90-99%              107.92478  347.05109   0.311    0.756
stenose100% (Occlusion)    -10.79076  439.90155  -0.025    0.980

Residual standard error: 336.7 on 180 degrees of freedom
Multiple R-squared:  0.04011,   Adjusted R-squared:  -0.05055 
F-statistic: 0.4424 on 17 and 180 DF,  p-value: 0.9731

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: RANTES_rank 
Effect size...............: 20.40994 
Standard error............: 27.49949 
Odds ratio (effect size)..: 731011166 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.870506e+32 
T-value...................: 0.742193 
P-value...................: 0.4589375 
R^2.......................: 0.040108 
Adjusted r^2..............: -0.050548 
Sample size of AE DB......: 622 
Sample size of model......: 198 
Missing data %............: 68.1672 

- processing MIG_rank
filter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]            GFR_MDRD  
            373.06               43.62               -1.92  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-342.66 -144.88  -51.64   45.32 3068.02 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                341.37410 1006.47413   0.339   0.7349  
currentDF[, TRAIT]          52.50181   27.07193   1.939   0.0540 .
Age                         -0.47832    3.45081  -0.139   0.8899  
Gendermale                  17.11142   58.56656   0.292   0.7705  
ORdate_epoch                 0.01251    0.06769   0.185   0.8536  
Hypertension.compositeyes   33.40183   77.64654   0.430   0.6676  
DiabetesStatusDiabetes     -83.47022   63.51663  -1.314   0.1905  
SmokerStatusEx-smoker      -48.98919   56.81833  -0.862   0.3897  
SmokerStatusNever smoked  -102.86265   74.34930  -1.384   0.1682  
Med.Statin.LLDyes          -61.75967   56.82714  -1.087   0.2786  
Med.all.antiplateletyes    -80.33915   89.32081  -0.899   0.3696  
GFR_MDRD                    -2.31071    1.34201  -1.722   0.0868 .
BMI                         -0.92000    6.26735  -0.147   0.8835  
MedHx_CVDNo                  8.05694   52.01816   0.155   0.8771  
stenose50-70%               47.09097  424.25622   0.111   0.9117  
stenose70-90%               97.88110  343.14204   0.285   0.7758  
stenose90-99%               79.37698  342.35728   0.232   0.8169  
stenose100% (Occlusion)      8.52115  431.12799   0.020   0.9843  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 331.9 on 181 degrees of freedom
Multiple R-squared:  0.05856,   Adjusted R-squared:  -0.02987 
F-statistic: 0.6622 on 17 and 181 DF,  p-value: 0.8368

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MIG_rank 
Effect size...............: 52.50181 
Standard error............: 27.07193 
Odds ratio (effect size)..: 6.327687e+22 
Lower 95% CI..............: 0.572 
Upper 95% CI..............: 7.003908e+45 
T-value...................: 1.939345 
P-value...................: 0.05401412 
R^2.......................: 0.058556 
Adjusted r^2..............: -0.029867 
Sample size of AE DB......: 622 
Sample size of model......: 199 
Missing data %............: 68.00643 

- processing IP10_rank
filter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            233.87               40.69  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-337.03 -135.20  -55.33   53.99 3058.38 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -236.03124  969.55781  -0.243   0.8080  
currentDF[, TRAIT]          48.28820   25.95751   1.860   0.0646 .
Age                         -2.55084    3.54275  -0.720   0.4725  
Gendermale                  64.11111   58.15825   1.102   0.2719  
ORdate_epoch                 0.05645    0.06378   0.885   0.3774  
Hypertension.compositeyes   88.21497   78.73028   1.120   0.2641  
DiabetesStatusDiabetes     -78.88847   65.96501  -1.196   0.2334  
SmokerStatusEx-smoker      -41.83193   58.69960  -0.713   0.4771  
SmokerStatusNever smoked   -79.09848   75.60730  -1.046   0.2970  
Med.Statin.LLDyes          -50.40762   58.20576  -0.866   0.3877  
Med.all.antiplateletyes      8.50161   90.14359   0.094   0.9250  
GFR_MDRD                    -1.89645    1.38404  -1.370   0.1725  
BMI                         -2.93184    6.26835  -0.468   0.6406  
MedHx_CVDNo                -31.99951   53.27840  -0.601   0.5489  
stenose50-70%               52.94048  414.52316   0.128   0.8985  
stenose70-90%               99.43545  336.35188   0.296   0.7679  
stenose90-99%              108.87242  334.44273   0.326   0.7452  
stenose100% (Occlusion)    147.35399  422.48970   0.349   0.7277  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 324.1 on 165 degrees of freedom
Multiple R-squared:  0.06349,   Adjusted R-squared:  -0.033 
F-statistic: 0.658 on 17 and 165 DF,  p-value: 0.8402

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: IP10_rank 
Effect size...............: 48.2882 
Standard error............: 25.95751 
Odds ratio (effect size)..: 9.360536e+20 
Lower 95% CI..............: 0.075 
Upper 95% CI..............: 1.166211e+43 
T-value...................: 1.860279 
P-value...................: 0.06462592 
R^2.......................: 0.063486 
Adjusted r^2..............: -0.033003 
Sample size of AE DB......: 622 
Sample size of model......: 183 
Missing data %............: 70.57878 

- processing Eotaxin1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-352.85 -143.19  -50.72   34.02 3098.84 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)
(Intercept)                41.38963  981.54113   0.042    0.966
currentDF[, TRAIT]         31.20422   25.17114   1.240    0.217
Age                        -1.08891    3.40974  -0.319    0.750
Gendermale                 23.08163   58.07732   0.397    0.692
ORdate_epoch                0.03701    0.06523   0.567    0.571
Hypertension.compositeyes  28.17463   75.32464   0.374    0.709
DiabetesStatusDiabetes    -79.36488   62.50306  -1.270    0.206
SmokerStatusEx-smoker     -37.84302   56.05104  -0.675    0.500
SmokerStatusNever smoked  -91.42957   73.51167  -1.244    0.215
Med.Statin.LLDyes         -62.39457   56.06652  -1.113    0.267
Med.all.antiplateletyes   -79.01301   87.51684  -0.903    0.368
GFR_MDRD                   -2.07213    1.33757  -1.549    0.123
BMI                        -0.76996    6.26185  -0.123    0.902
MedHx_CVDNo                11.12579   51.17734   0.217    0.828
stenose50-70%              77.91366  395.77120   0.197    0.844
stenose70-90%             101.14750  343.29696   0.295    0.769
stenose90-99%              88.13261  342.93390   0.257    0.797
stenose100% (Occlusion)     0.16109  430.54559   0.000    1.000

Residual standard error: 332.2 on 184 degrees of freedom
Multiple R-squared:  0.04563,   Adjusted R-squared:  -0.04255 
F-statistic: 0.5175 on 17 and 184 DF,  p-value: 0.9422

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 31.20422 
Standard error............: 25.17114 
Odds ratio (effect size)..: 3.563025e+13 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.50437e+34 
T-value...................: 1.239682 
P-value...................: 0.2166716 
R^2.......................: 0.045627 
Adjusted r^2..............: -0.042549 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing TARC_rank
filter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      248.3  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-335.69 -152.80  -58.95   39.72 3073.30 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -672.37946 1228.63525  -0.547    0.585
currentDF[, TRAIT]          44.81707   29.42016   1.523    0.130
Age                          0.14609    3.89090   0.038    0.970
Gendermale                  44.50452   65.58759   0.679    0.498
ORdate_epoch                 0.08434    0.08523   0.990    0.324
Hypertension.compositeyes   43.81156   82.67150   0.530    0.597
DiabetesStatusDiabetes     -86.91134   69.20873  -1.256    0.211
SmokerStatusEx-smoker      -36.54594   62.78941  -0.582    0.561
SmokerStatusNever smoked   -99.98010   80.22880  -1.246    0.215
Med.Statin.LLDyes          -86.65601   65.29743  -1.327    0.186
Med.all.antiplateletyes    -76.72540  101.19983  -0.758    0.449
GFR_MDRD                    -1.90164    1.51536  -1.255    0.211
BMI                         -1.45280    6.99756  -0.208    0.836
MedHx_CVDNo                  9.75870   57.62889   0.169    0.866
stenose50-70%               94.96007  442.48460   0.215    0.830
stenose70-90%              148.70151  361.54597   0.411    0.681
stenose90-99%              113.85259  360.33641   0.316    0.752
stenose100% (Occlusion)     53.35200  458.85084   0.116    0.908

Residual standard error: 349.5 on 160 degrees of freedom
Multiple R-squared:  0.05629,   Adjusted R-squared:  -0.04398 
F-statistic: 0.5614 on 17 and 160 DF,  p-value: 0.9158

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: TARC_rank 
Effect size...............: 44.81707 
Standard error............: 29.42016 
Odds ratio (effect size)..: 2.909413e+19 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.211846e+44 
T-value...................: 1.523345 
P-value...................: 0.1296464 
R^2.......................: 0.056287 
Adjusted r^2..............: -0.043983 
Sample size of AE DB......: 622 
Sample size of model......: 178 
Missing data %............: 71.38264 

- processing PARC_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-338.31 -147.06  -62.63   37.37 3061.38 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -633.23658 1047.38862  -0.605    0.546
currentDF[, TRAIT]          31.92799   26.64862   1.198    0.232
Age                         -1.30160    3.39230  -0.384    0.702
Gendermale                  34.42606   57.66172   0.597    0.551
ORdate_epoch                 0.08478    0.06951   1.220    0.224
Hypertension.compositeyes   42.66098   76.26176   0.559    0.577
DiabetesStatusDiabetes     -80.44236   62.46900  -1.288    0.199
SmokerStatusEx-smoker      -33.42161   55.81839  -0.599    0.550
SmokerStatusNever smoked   -89.38284   73.32308  -1.219    0.224
Med.Statin.LLDyes          -61.24124   56.03752  -1.093    0.276
Med.all.antiplateletyes    -66.27085   87.54919  -0.757    0.450
GFR_MDRD                    -1.90200    1.34879  -1.410    0.160
BMI                         -0.28922    6.29272  -0.046    0.963
MedHx_CVDNo                  5.03829   51.12627   0.099    0.922
stenose50-70%              110.40154  394.99516   0.280    0.780
stenose70-90%              121.39252  342.70310   0.354    0.724
stenose90-99%              125.83260  341.41359   0.369    0.713
stenose100% (Occlusion)     64.94744  431.49605   0.151    0.881

Residual standard error: 332.3 on 184 degrees of freedom
Multiple R-squared:  0.04511,   Adjusted R-squared:  -0.04312 
F-statistic: 0.5113 on 17 and 184 DF,  p-value: 0.9453

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: PARC_rank 
Effect size...............: 31.92799 
Standard error............: 26.64862 
Odds ratio (effect size)..: 7.347673e+13 
Lower 95% CI..............: 0 
Upper 95% CI..............: 3.547398e+36 
T-value...................: 1.198111 
P-value...................: 0.2324149 
R^2.......................: 0.045105 
Adjusted r^2..............: -0.043119 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MDC_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            235.74               41.62  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-324.85 -139.76  -48.50   49.19 3053.28 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -632.42256  974.06990  -0.649   0.5170  
currentDF[, TRAIT]          49.10940   25.60441   1.918   0.0568 .
Age                         -2.55300    3.35583  -0.761   0.4478  
Gendermale                  57.17186   57.77451   0.990   0.3238  
ORdate_epoch                 0.08383    0.06503   1.289   0.1991  
Hypertension.compositeyes   88.63692   76.13924   1.164   0.2460  
DiabetesStatusDiabetes     -65.08819   63.33261  -1.028   0.3055  
SmokerStatusEx-smoker      -11.47604   55.99669  -0.205   0.8379  
SmokerStatusNever smoked   -72.44354   72.09595  -1.005   0.3164  
Med.Statin.LLDyes          -57.85666   56.81205  -1.018   0.3099  
Med.all.antiplateletyes     17.02501   94.53368   0.180   0.8573  
GFR_MDRD                    -1.41316    1.30781  -1.081   0.2814  
BMI                         -2.30237    6.24818  -0.368   0.7130  
MedHx_CVDNo                -10.74087   52.13389  -0.206   0.8370  
stenose50-70%               10.53659  409.20891   0.026   0.9795  
stenose70-90%               83.65712  330.59586   0.253   0.8005  
stenose90-99%               88.94072  329.47058   0.270   0.7875  
stenose100% (Occlusion)     82.31782  416.64127   0.198   0.8436  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 319.3 on 171 degrees of freedom
Multiple R-squared:  0.05936,   Adjusted R-squared:  -0.03415 
F-statistic: 0.6348 on 17 and 171 DF,  p-value: 0.8608

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MDC_rank 
Effect size...............: 49.1094 
Standard error............: 25.60441 
Odds ratio (effect size)..: 2.127856e+21 
Lower 95% CI..............: 0.341 
Upper 95% CI..............: 1.326962e+43 
T-value...................: 1.918005 
P-value...................: 0.05677668 
R^2.......................: 0.059363 
Adjusted r^2..............: -0.034151 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing OPG_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-346.34 -149.14  -63.96   32.10 3122.04 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -144.48733  974.35872  -0.148    0.882
currentDF[, TRAIT]          -4.69377   24.36674  -0.193    0.847
Age                         -1.78057    3.44061  -0.518    0.605
Gendermale                  32.84300   57.96043   0.567    0.572
ORdate_epoch                 0.05217    0.06431   0.811    0.418
Hypertension.compositeyes   27.28580   75.90721   0.359    0.720
DiabetesStatusDiabetes     -85.50706   62.60091  -1.366    0.174
SmokerStatusEx-smoker      -29.22470   56.54791  -0.517    0.606
SmokerStatusNever smoked   -73.77177   73.39702  -1.005    0.316
Med.Statin.LLDyes          -57.69002   56.26460  -1.025    0.307
Med.all.antiplateletyes    -71.04248   88.10161  -0.806    0.421
GFR_MDRD                    -2.10992    1.34266  -1.571    0.118
BMI                         -1.17641    6.30674  -0.187    0.852
MedHx_CVDNo                  7.27109   51.28855   0.142    0.887
stenose50-70%              112.61597  396.61922   0.284    0.777
stenose70-90%              128.40141  343.96910   0.373    0.709
stenose90-99%              129.46053  342.69451   0.378    0.706
stenose100% (Occlusion)     18.34790  433.17245   0.042    0.966

Residual standard error: 333.6 on 184 degrees of freedom
Multiple R-squared:  0.03785,   Adjusted R-squared:  -0.05104 
F-statistic: 0.4258 on 17 and 184 DF,  p-value: 0.978

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: OPG_rank 
Effect size...............: -4.693769 
Standard error............: 24.36674 
Odds ratio (effect size)..: 0.009 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.045526e+18 
T-value...................: -0.19263 
P-value...................: 0.8474611 
R^2.......................: 0.03785 
Adjusted r^2..............: -0.051045 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing sICAM1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      241.9  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-354.44 -145.83  -57.59   35.14 3090.30 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -385.45750  996.02109  -0.387    0.699
currentDF[, TRAIT]          25.81962   25.54712   1.011    0.314
Age                         -0.97238    3.45167  -0.282    0.778
Gendermale                  30.09948   57.73059   0.521    0.603
ORdate_epoch                 0.06892    0.06617   1.042    0.299
Hypertension.compositeyes   36.57434   75.84734   0.482    0.630
DiabetesStatusDiabetes     -80.43930   62.58970  -1.285    0.200
SmokerStatusEx-smoker      -33.02493   55.88151  -0.591    0.555
SmokerStatusNever smoked   -88.67676   73.63686  -1.204    0.230
Med.Statin.LLDyes          -59.32121   56.05421  -1.058    0.291
Med.all.antiplateletyes    -75.75228   87.54124  -0.865    0.388
GFR_MDRD                    -1.97792    1.34543  -1.470    0.143
BMI                         -1.25520    6.26854  -0.200    0.842
MedHx_CVDNo                 13.11488   51.48313   0.255    0.799
stenose50-70%               92.69093  395.83752   0.234    0.815
stenose70-90%               96.26706  344.57435   0.279    0.780
stenose90-99%               98.11041  343.18979   0.286    0.775
stenose100% (Occlusion)      0.04103  431.38339   0.000    1.000

Residual standard error: 332.7 on 184 degrees of freedom
Multiple R-squared:  0.04297,   Adjusted R-squared:  -0.04545 
F-statistic: 0.4859 on 17 and 184 DF,  p-value: 0.9571

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: sICAM1_rank 
Effect size...............: 25.81962 
Standard error............: 25.54712 
Odds ratio (effect size)..: 163424446378 
Lower 95% CI..............: 0 
Upper 95% CI..............: 9.10881e+32 
T-value...................: 1.010667 
P-value...................: 0.3135032 
R^2.......................: 0.042968 
Adjusted r^2..............: -0.045453 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing VEGFA_rank
filter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      246.8  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-338.68 -151.21  -70.42   37.67 3124.10 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -227.62798 1106.14409  -0.206    0.837
currentDF[, TRAIT]          -1.00134   29.61715  -0.034    0.973
Age                         -0.49405    3.87624  -0.127    0.899
Gendermale                  35.66493   65.72023   0.543    0.588
ORdate_epoch                 0.06318    0.08222   0.768    0.443
Hypertension.compositeyes   30.34132   87.80973   0.346    0.730
DiabetesStatusDiabetes     -93.46439   70.50848  -1.326    0.187
SmokerStatusEx-smoker      -21.78413   62.68701  -0.348    0.729
SmokerStatusNever smoked   -59.06716   85.85415  -0.688    0.492
Med.Statin.LLDyes          -58.67762   63.01597  -0.931    0.353
Med.all.antiplateletyes    -65.23866   94.12293  -0.693    0.489
GFR_MDRD                    -2.03889    1.39327  -1.463    0.145
BMI                         -1.63872    7.20873  -0.227    0.820
MedHx_CVDNo                 11.43260   58.52723   0.195    0.845
stenose70-90%               -5.77619  269.98559  -0.021    0.983
stenose90-99%              -22.92581  267.46881  -0.086    0.932
stenose100% (Occlusion)   -110.20429  380.77216  -0.289    0.773

Residual standard error: 352 on 160 degrees of freedom
Multiple R-squared:  0.03699,   Adjusted R-squared:  -0.05931 
F-statistic: 0.3841 on 16 and 160 DF,  p-value: 0.9846

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -1.001337 
Standard error............: 29.61715 
Odds ratio (effect size)..: 0.367 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.966993e+24 
T-value...................: -0.033809 
P-value...................: 0.9730713 
R^2.......................: 0.036992 
Adjusted r^2..............: -0.059309 
Sample size of AE DB......: 622 
Sample size of model......: 177 
Missing data %............: 71.54341 

- processing TGFB_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD, 
    data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes                GFR_MDRD  
               386.186                 -83.192                  -1.748  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-349.98 -151.60  -57.84   43.76 3117.85 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -147.68278  963.52354  -0.153    0.878
currentDF[, TRAIT]           6.87291   24.45742   0.281    0.779
Age                         -1.73273    3.41640  -0.507    0.613
Gendermale                  24.81947   57.32164   0.433    0.666
ORdate_epoch                 0.05489    0.06399   0.858    0.392
Hypertension.compositeyes   38.53812   74.52005   0.517    0.606
DiabetesStatusDiabetes     -91.74773   60.76243  -1.510    0.133
SmokerStatusEx-smoker      -21.72899   55.81681  -0.389    0.698
SmokerStatusNever smoked   -75.19337   74.03820  -1.016    0.311
Med.Statin.LLDyes          -51.97702   56.00993  -0.928    0.355
Med.all.antiplateletyes    -86.32257   90.02816  -0.959    0.339
GFR_MDRD                    -2.15082    1.33576  -1.610    0.109
BMI                         -1.82763    6.32021  -0.289    0.773
MedHx_CVDNo                  8.26504   51.12194   0.162    0.872
stenose50-70%              105.54088  397.98398   0.265    0.791
stenose70-90%              125.27842  343.73077   0.364    0.716
stenose90-99%              123.03531  342.57136   0.359    0.720
stenose100% (Occlusion)     -6.10423  434.55960  -0.014    0.989

Residual standard error: 332.6 on 185 degrees of freedom
Multiple R-squared:  0.04018,   Adjusted R-squared:  -0.04802 
F-statistic: 0.4555 on 17 and 185 DF,  p-value: 0.9688

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: TGFB_rank 
Effect size...............: 6.872906 
Standard error............: 24.45742 
Odds ratio (effect size)..: 965.751 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.359778e+23 
T-value...................: 0.281015 
P-value...................: 0.7790128 
R^2.......................: 0.040178 
Adjusted r^2..............: -0.048022 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing MMP2_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)

Coefficients:
(Intercept)     GFR_MDRD  
    360.725       -1.721  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-318.42 -147.02  -56.17   47.40 3111.58 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               257.43781  990.00895   0.260   0.7951  
currentDF[, TRAIT]        -25.01480   25.27122  -0.990   0.3235  
Age                        -2.41784    3.36999  -0.717   0.4740  
Gendermale                  8.64954   57.00626   0.152   0.8796  
ORdate_epoch                0.02981    0.06535   0.456   0.6489  
Hypertension.compositeyes  15.20505   77.07375   0.197   0.8438  
DiabetesStatusDiabetes    -88.25297   61.72145  -1.430   0.1545  
SmokerStatusEx-smoker     -13.08655   55.83633  -0.234   0.8150  
SmokerStatusNever smoked  -57.34041   73.72530  -0.778   0.4377  
Med.Statin.LLDyes         -62.45485   55.45371  -1.126   0.2615  
Med.all.antiplateletyes   -69.22437   87.06919  -0.795   0.4276  
GFR_MDRD                   -2.40356    1.29184  -1.861   0.0644 .
BMI                        -2.19867    6.42424  -0.342   0.7326  
MedHx_CVDNo                 4.94581   51.14162   0.097   0.9231  
stenose50-70%             121.03737  394.20060   0.307   0.7592  
stenose70-90%             122.04580  342.20859   0.357   0.7218  
stenose90-99%             117.81449  340.73689   0.346   0.7299  
stenose100% (Occlusion)    -0.76762  429.87811  -0.002   0.9986  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 331.3 on 184 degrees of freedom
Multiple R-squared:  0.04399,   Adjusted R-squared:  -0.04434 
F-statistic: 0.498 on 17 and 184 DF,  p-value: 0.9517

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MMP2_rank 
Effect size...............: -25.0148 
Standard error............: 25.27123 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 44413097706 
T-value...................: -0.989853 
P-value...................: 0.3235458 
R^2.......................: 0.043988 
Adjusted r^2..............: -0.044339 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP8_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            240.08               47.11  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-411.85 -134.97  -55.57   51.85 3114.37 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                216.92798  968.82891   0.224   0.8231  
currentDF[, TRAIT]          42.83720   24.27166   1.765   0.0792 .
Age                         -1.69719    3.34072  -0.508   0.6120  
Gendermale                  -1.51679   57.07025  -0.027   0.9788  
ORdate_epoch                 0.03691    0.06395   0.577   0.5646  
Hypertension.compositeyes   27.03879   75.65207   0.357   0.7212  
DiabetesStatusDiabetes     -75.77466   61.83690  -1.225   0.2220  
SmokerStatusEx-smoker      -11.67688   55.44710  -0.211   0.8334  
SmokerStatusNever smoked   -66.31237   72.64636  -0.913   0.3625  
Med.Statin.LLDyes          -61.29266   55.13420  -1.112   0.2677  
Med.all.antiplateletyes    -67.15497   86.49929  -0.776   0.4385  
GFR_MDRD                    -1.88586    1.28908  -1.463   0.1452  
BMI                         -2.68692    6.39352  -0.420   0.6748  
MedHx_CVDNo                 15.30870   51.08063   0.300   0.7647  
stenose50-70%               10.18413  394.76483   0.026   0.9794  
stenose70-90%              -19.64721  348.13793  -0.056   0.9551  
stenose90-99%               -8.39482  345.31447  -0.024   0.9806  
stenose100% (Occlusion)   -142.91578  435.06622  -0.328   0.7429  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 329.4 on 184 degrees of freedom
Multiple R-squared:  0.0549,    Adjusted R-squared:  -0.03242 
F-statistic: 0.6287 on 17 and 184 DF,  p-value: 0.8664

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MMP8_rank 
Effect size...............: 42.8372 
Standard error............: 24.27166 
Odds ratio (effect size)..: 4.017524e+18 
Lower 95% CI..............: 0.009 
Upper 95% CI..............: 1.838277e+39 
T-value...................: 1.764906 
P-value...................: 0.07923853 
R^2.......................: 0.054897 
Adjusted r^2..............: -0.032423 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP9_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            239.91               51.04  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-407.14 -139.32  -59.91   51.47 3085.93 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -191.03538  968.13355  -0.197   0.8438  
currentDF[, TRAIT]          51.62078   23.87100   2.162   0.0319 *
Age                         -1.34039    3.33743  -0.402   0.6884  
Gendermale                   7.78308   56.10389   0.139   0.8898  
ORdate_epoch                 0.05838    0.06411   0.911   0.3637  
Hypertension.compositeyes   55.43247   76.44738   0.725   0.4693  
DiabetesStatusDiabetes     -72.64380   61.58908  -1.179   0.2397  
SmokerStatusEx-smoker      -20.44436   55.14532  -0.371   0.7113  
SmokerStatusNever smoked   -78.56971   72.53794  -1.083   0.2802  
Med.Statin.LLDyes          -66.19372   54.94111  -1.205   0.2298  
Med.all.antiplateletyes    -86.73659   86.21115  -1.006   0.3157  
GFR_MDRD                    -1.74384    1.28869  -1.353   0.1777  
BMI                         -2.37232    6.35142  -0.374   0.7092  
MedHx_CVDNo                 15.79691   50.80552   0.311   0.7562  
stenose50-70%               84.49535  389.86468   0.217   0.8287  
stenose70-90%               76.28124  339.07694   0.225   0.8223  
stenose90-99%               80.07091  337.58799   0.237   0.8128  
stenose100% (Occlusion)    -10.88774  425.67670  -0.026   0.9796  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 328 on 184 degrees of freedom
Multiple R-squared:  0.06272,   Adjusted R-squared:  -0.02388 
F-statistic: 0.7243 on 17 and 184 DF,  p-value: 0.7755

Analyzing in dataset ' AEDB.CEA ' the association of ' LDLR ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: LDLR 
Trait/outcome.............: MMP9_rank 
Effect size...............: 51.62078 
Standard error............: 23.871 
Odds ratio (effect size)..: 2.62193e+22 
Lower 95% CI..............: 125.665 
Upper 95% CI..............: 5.470522e+42 
T-value...................: 2.162489 
P-value...................: 0.0318711 
R^2.......................: 0.062718 
Adjusted r^2..............: -0.023878 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

Analysis of CD36.

- processing IL2_rank
filter: removed 459 rows (74%), 163 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
  -949.75469       0.08933     -50.15658  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
   Min     1Q Median     3Q    Max 
-208.5 -116.7  -55.9   37.0  866.0 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -833.56645  639.51509  -1.303   0.1945  
currentDF[, TRAIT]         -11.88969   16.52742  -0.719   0.4730  
Age                         -0.84475    2.20758  -0.383   0.7025  
Gendermale                  -7.79328   39.71358  -0.196   0.8447  
ORdate_epoch                 0.09291    0.04561   2.037   0.0435 *
Hypertension.compositeyes   12.33547   51.70666   0.239   0.8118  
DiabetesStatusDiabetes     -10.01436   43.32640  -0.231   0.8175  
SmokerStatusEx-smoker        7.64400   37.13123   0.206   0.8372  
SmokerStatusNever smoked   -37.80549   50.55629  -0.748   0.4558  
Med.Statin.LLDyes          -27.08885   36.47605  -0.743   0.4589  
Med.all.antiplateletyes    -28.59872   57.80041  -0.495   0.6215  
GFR_MDRD                    -0.53303    0.94856  -0.562   0.5750  
BMI                         -3.54894    4.92142  -0.721   0.4720  
MedHx_CVDNo                -47.06710   34.50970  -1.364   0.1747  
stenose70-90%               68.03468  147.65630   0.461   0.6457  
stenose90-99%               75.93403  146.71074   0.518   0.6055  
stenose100% (Occlusion)    -27.13955  213.68709  -0.127   0.8991  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 196.9 on 146 degrees of freedom
Multiple R-squared:  0.05769,   Adjusted R-squared:  -0.04557 
F-statistic: 0.5587 on 16 and 146 DF,  p-value: 0.9098

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL2_rank 
Effect size...............: -11.88969 
Standard error............: 16.52742 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 803152200 
T-value...................: -0.719392 
P-value...................: 0.4730496 
R^2.......................: 0.057692 
Adjusted r^2..............: -0.045574 
Sample size of AE DB......: 622 
Sample size of model......: 163 
Missing data %............: 73.79421 

- processing IL4_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
  -1201.4130        0.1102      -56.7764  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-231.75 -127.26  -55.59   34.59  860.24 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.142e+03  7.342e+02  -1.555   0.1224  
currentDF[, TRAIT]        -1.978e+00  1.896e+01  -0.104   0.9171  
Age                       -5.899e-01  2.484e+00  -0.237   0.8127  
Gendermale                -1.580e+00  4.438e+01  -0.036   0.9716  
ORdate_epoch               1.172e-01  5.267e-02   2.224   0.0279 *
Hypertension.compositeyes  1.515e+01  5.829e+01   0.260   0.7953  
DiabetesStatusDiabetes     2.295e+00  4.849e+01   0.047   0.9623  
SmokerStatusEx-smoker      3.434e+00  4.045e+01   0.085   0.9325  
SmokerStatusNever smoked  -3.418e+01  5.748e+01  -0.595   0.5531  
Med.Statin.LLDyes         -3.468e+01  4.243e+01  -0.817   0.4152  
Med.all.antiplateletyes   -1.457e+01  6.368e+01  -0.229   0.8194  
GFR_MDRD                  -7.768e-01  1.157e+00  -0.671   0.5031  
BMI                       -3.908e+00  5.794e+00  -0.674   0.5012  
MedHx_CVDNo               -5.293e+01  3.860e+01  -1.371   0.1727  
stenose70-90%              9.045e+01  1.570e+02   0.576   0.5656  
stenose90-99%              7.807e+01  1.558e+02   0.501   0.6171  
stenose100% (Occlusion)   -4.452e+00  2.276e+02  -0.020   0.9844  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 206.9 on 128 degrees of freedom
Multiple R-squared:  0.06752,   Adjusted R-squared:  -0.04904 
F-statistic: 0.5793 on 16 and 128 DF,  p-value: 0.8947

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL4_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL4_rank 
Effect size...............: -1.977614 
Standard error............: 18.95543 
Odds ratio (effect size)..: 0.138 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.88937e+15 
T-value...................: -0.10433 
P-value...................: 0.917071 
R^2.......................: 0.067521 
Adjusted r^2..............: -0.049038 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing IL5_rank
filter: removed 464 rows (75%), 158 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
  -996.25226       0.09303     -51.18540  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-203.63 -114.26  -57.87   35.17  879.25 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -829.23484  666.70865  -1.244   0.2156  
currentDF[, TRAIT]         -11.56428   16.99859  -0.680   0.4974  
Age                         -0.58578    2.35273  -0.249   0.8037  
Gendermale                   0.31920   41.03179   0.008   0.9938  
ORdate_epoch                 0.09026    0.04864   1.856   0.0656 .
Hypertension.compositeyes    7.85714   54.37057   0.145   0.8853  
DiabetesStatusDiabetes      -6.47053   44.19658  -0.146   0.8838  
SmokerStatusEx-smoker        0.05622   37.37777   0.002   0.9988  
SmokerStatusNever smoked   -31.26109   53.98021  -0.579   0.5634  
Med.Statin.LLDyes          -21.50863   38.45416  -0.559   0.5768  
Med.all.antiplateletyes    -26.59957   59.56613  -0.447   0.6559  
GFR_MDRD                    -0.45929    1.02146  -0.450   0.6537  
BMI                         -2.08990    5.07086  -0.412   0.6809  
MedHx_CVDNo                -47.58267   36.27158  -1.312   0.1917  
stenose70-90%               35.32848  125.54672   0.281   0.7788  
stenose90-99%               32.03121  124.01379   0.258   0.7966  
stenose100% (Occlusion)    -61.36701  200.42559  -0.306   0.7599  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 200.8 on 141 degrees of freedom
Multiple R-squared:  0.0497,    Adjusted R-squared:  -0.05813 
F-statistic: 0.4609 on 16 and 141 DF,  p-value: 0.9614

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL5_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL5_rank 
Effect size...............: -11.56428 
Standard error............: 16.99859 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2800191696 
T-value...................: -0.680308 
P-value...................: 0.4974249 
R^2.......................: 0.049701 
Adjusted r^2..............: -0.058135 
Sample size of AE DB......: 622 
Sample size of model......: 158 
Missing data %............: 74.59807 

- processing IL6_rank
filter: removed 458 rows (74%), 164 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
   -890.1511        0.0836      -43.0696  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-210.12 -105.36  -46.86   25.77  882.81 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.042e+03  6.374e+02  -1.636   0.1041  
currentDF[, TRAIT]        -1.205e+01  1.528e+01  -0.789   0.4313  
Age                       -1.051e+00  2.233e+00  -0.471   0.6386  
Gendermale                -1.651e+01  3.675e+01  -0.449   0.6539  
ORdate_epoch               9.902e-02  4.247e-02   2.332   0.0211 *
Hypertension.compositeyes  6.425e+00  4.910e+01   0.131   0.8961  
DiabetesStatusDiabetes     2.700e+00  4.108e+01   0.066   0.9477  
SmokerStatusEx-smoker      4.204e+00  3.524e+01   0.119   0.9052  
SmokerStatusNever smoked  -4.543e+01  4.890e+01  -0.929   0.3543  
Med.Statin.LLDyes         -3.726e+01  3.474e+01  -1.073   0.2852  
Med.all.antiplateletyes   -3.984e+01  5.633e+01  -0.707   0.4805  
GFR_MDRD                  -5.123e-01  9.494e-01  -0.540   0.5903  
BMI                       -9.806e-01  4.072e+00  -0.241   0.8100  
MedHx_CVDNo               -4.176e+01  3.312e+01  -1.261   0.2094  
stenose50-70%              1.348e+02  2.266e+02   0.595   0.5527  
stenose70-90%              1.624e+02  1.969e+02   0.825   0.4109  
stenose90-99%              1.704e+02  1.965e+02   0.867   0.3874  
stenose100% (Occlusion)    8.305e+01  2.480e+02   0.335   0.7382  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 188.9 on 146 degrees of freedom
Multiple R-squared:  0.06428,   Adjusted R-squared:  -0.04467 
F-statistic:  0.59 on 17 and 146 DF,  p-value: 0.8954

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL6_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL6_rank 
Effect size...............: -12.05388 
Standard error............: 15.27504 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 58538544 
T-value...................: -0.789123 
P-value...................: 0.4313199 
R^2.......................: 0.06428 
Adjusted r^2..............: -0.044673 
Sample size of AE DB......: 622 
Sample size of model......: 164 
Missing data %............: 73.63344 

- processing IL8_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch + 
    MedHx_CVD, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch         MedHx_CVDNo  
        -689.53500            25.03377             0.06852           -50.67884  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-220.34 -110.86  -49.13   48.42  861.74 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -960.00274  698.95086  -1.373   0.1719  
currentDF[, TRAIT]          29.63236   16.89164   1.754   0.0816 .
Age                          0.23306    2.28606   0.102   0.9189  
Gendermale                 -29.00854   40.81907  -0.711   0.4785  
ORdate_epoch                 0.08472    0.04652   1.821   0.0708 .
Hypertension.compositeyes    5.68117   50.63307   0.112   0.9108  
DiabetesStatusDiabetes      -5.42485   42.89174  -0.126   0.8995  
SmokerStatusEx-smoker        0.54408   38.32077   0.014   0.9887  
SmokerStatusNever smoked   -56.86648   55.15094  -1.031   0.3043  
Med.Statin.LLDyes          -28.16969   36.69756  -0.768   0.4440  
Med.all.antiplateletyes    -13.73557   56.53932  -0.243   0.8084  
GFR_MDRD                    -0.54721    0.90424  -0.605   0.5461  
BMI                         -0.50697    4.32054  -0.117   0.9068  
MedHx_CVDNo                -42.78600   35.77129  -1.196   0.2337  
stenose50-70%              154.03855  234.25201   0.658   0.5119  
stenose70-90%              135.37351  204.57953   0.662   0.5093  
stenose90-99%              177.02615  203.83540   0.868   0.3867  
stenose100% (Occlusion)    128.02218  291.00996   0.440   0.6607  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 195.6 on 136 degrees of freedom
Multiple R-squared:  0.09426,   Adjusted R-squared:  -0.01895 
F-statistic: 0.8326 on 17 and 136 DF,  p-value: 0.6534

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL8_rank 
Effect size...............: 29.63236 
Standard error............: 16.89164 
Odds ratio (effect size)..: 7.398963e+12 
Lower 95% CI..............: 0.031 
Upper 95% CI..............: 1.768585e+27 
T-value...................: 1.754262 
P-value...................: 0.08163803 
R^2.......................: 0.094262 
Adjusted r^2..............: -0.018955 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing IL9_rank
filter: removed 436 rows (70%), 186 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -596.89466            27.36194             0.05932  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-267.13 -105.46  -53.02   30.86  876.34 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -898.55787  583.93956  -1.539   0.1257  
currentDF[, TRAIT]          28.82261   14.70503   1.960   0.0516 .
Age                          0.68701    2.14016   0.321   0.7486  
Gendermale                  -7.26807   35.47764  -0.205   0.8379  
ORdate_epoch                 0.07349    0.03826   1.921   0.0564 .
Hypertension.compositeyes   30.90120   47.90383   0.645   0.5198  
DiabetesStatusDiabetes     -14.37190   39.65114  -0.362   0.7175  
SmokerStatusEx-smoker      -14.76794   34.65873  -0.426   0.6706  
SmokerStatusNever smoked   -49.76201   44.13368  -1.128   0.2611  
Med.Statin.LLDyes           -6.58869   35.04578  -0.188   0.8511  
Med.all.antiplateletyes    -27.92922   57.93817  -0.482   0.6304  
GFR_MDRD                     0.03106    0.80625   0.039   0.9693  
BMI                         -1.15085    3.90391  -0.295   0.7685  
MedHx_CVDNo                -27.10743   32.03388  -0.846   0.3986  
stenose50-70%               88.53558  249.32085   0.355   0.7230  
stenose70-90%              140.44525  202.04417   0.695   0.4879  
stenose90-99%              146.53001  201.19315   0.728   0.4674  
stenose100% (Occlusion)     95.84826  255.92300   0.375   0.7085  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 195.4 on 168 degrees of freedom
Multiple R-squared:  0.05822,   Adjusted R-squared:  -0.03707 
F-statistic: 0.611 on 17 and 168 DF,  p-value: 0.8803

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL9_rank 
Effect size...............: 28.82261 
Standard error............: 14.70503 
Odds ratio (effect size)..: 3.292306e+12 
Lower 95% CI..............: 1.001 
Upper 95% CI..............: 1.083114e+25 
T-value...................: 1.960051 
P-value...................: 0.05164397 
R^2.......................: 0.058224 
Adjusted r^2..............: -0.037075 
Sample size of AE DB......: 622 
Sample size of model......: 186 
Missing data %............: 70.09646 

- processing IL10_rank
filter: removed 483 rows (78%), 139 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
  -1.073e+03     9.957e-02    -5.351e+01  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-231.23 -119.40  -56.07   33.06  878.98 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -957.88808  799.16361  -1.199   0.2330  
currentDF[, TRAIT]          -3.23438   19.40376  -0.167   0.8679  
Age                         -0.56358    2.60731  -0.216   0.8292  
Gendermale                  -7.51328   45.95202  -0.164   0.8704  
ORdate_epoch                 0.10082    0.05592   1.803   0.0739 .
Hypertension.compositeyes    7.60188   60.75392   0.125   0.9006  
DiabetesStatusDiabetes       0.39277   50.18812   0.008   0.9938  
SmokerStatusEx-smoker        0.82623   42.42096   0.019   0.9845  
SmokerStatusNever smoked   -27.32161   59.03562  -0.463   0.6443  
Med.Statin.LLDyes          -22.20267   42.30613  -0.525   0.6007  
Med.all.antiplateletyes    -24.56587   65.76869  -0.374   0.7094  
GFR_MDRD                    -0.77674    1.20579  -0.644   0.5207  
BMI                         -2.79626    5.95679  -0.469   0.6396  
MedHx_CVDNo                -49.98464   41.61717  -1.201   0.2321  
stenose70-90%               80.84853  160.51978   0.504   0.6154  
stenose90-99%               74.13248  159.23797   0.466   0.6424  
stenose100% (Occlusion)     -9.23424  232.90917  -0.040   0.9684  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 210.9 on 122 degrees of freedom
Multiple R-squared:  0.05361,   Adjusted R-squared:  -0.07051 
F-statistic: 0.4319 on 16 and 122 DF,  p-value: 0.9713

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL10_rank 
Effect size...............: -3.234382 
Standard error............: 19.40376 
Odds ratio (effect size)..: 0.039 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.294608e+15 
T-value...................: -0.166688 
P-value...................: 0.8678911 
R^2.......................: 0.053608 
Adjusted r^2..............: -0.070509 
Sample size of AE DB......: 622 
Sample size of model......: 139 
Missing data %............: 77.65273 

- processing IL12_rank
filter: removed 476 rows (77%), 146 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
  -1.055e+03     9.834e-02    -5.728e+01  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-227.09 -108.18  -54.87   26.20  842.86 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -841.05398  753.35447  -1.116   0.2663  
currentDF[, TRAIT]         -11.32035   18.32743  -0.618   0.5379  
Age                         -1.36833    2.52837  -0.541   0.5893  
Gendermale                  -6.39497   43.34574  -0.148   0.8829  
ORdate_epoch                 0.10030    0.05246   1.912   0.0581 .
Hypertension.compositeyes    5.71265   58.95456   0.097   0.9230  
DiabetesStatusDiabetes     -11.50397   46.96924  -0.245   0.8069  
SmokerStatusEx-smoker       -4.67117   41.68385  -0.112   0.9109  
SmokerStatusNever smoked   -36.54417   55.31040  -0.661   0.5100  
Med.Statin.LLDyes          -44.49186   41.48987  -1.072   0.2856  
Med.all.antiplateletyes    -33.25874   63.05851  -0.527   0.5988  
GFR_MDRD                    -0.68665    1.11601  -0.615   0.5395  
BMI                         -3.84222    5.64708  -0.680   0.4975  
MedHx_CVDNo                -59.63309   38.04154  -1.568   0.1194  
stenose70-90%               84.87555  154.41154   0.550   0.5835  
stenose90-99%               73.88707  153.79462   0.480   0.6317  
stenose100% (Occlusion)     -1.65105  270.55383  -0.006   0.9951  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 203.9 on 129 degrees of freedom
Multiple R-squared:  0.06562,   Adjusted R-squared:  -0.05028 
F-statistic: 0.5662 on 16 and 129 DF,  p-value: 0.904

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL12_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL12_rank 
Effect size...............: -11.32035 
Standard error............: 18.32743 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 48334895675 
T-value...................: -0.617672 
P-value...................: 0.5378801 
R^2.......................: 0.065616 
Adjusted r^2..............: -0.050277 
Sample size of AE DB......: 622 
Sample size of model......: 146 
Missing data %............: 76.52733 

- processing IL13_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -536.63910            20.70733             0.05432  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-254.34 -101.93  -58.19   18.77  887.91 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -815.88849  562.64459  -1.450   0.1487  
currentDF[, TRAIT]          25.38908   14.49443   1.752   0.0815 .
Age                          1.14661    2.00090   0.573   0.5673  
Gendermale                 -15.03724   33.55999  -0.448   0.6546  
ORdate_epoch                 0.06771    0.03733   1.814   0.0713 .
Hypertension.compositeyes   19.01206   43.82387   0.434   0.6649  
DiabetesStatusDiabetes     -16.67881   36.28152  -0.460   0.6463  
SmokerStatusEx-smoker      -19.45102   32.73371  -0.594   0.5531  
SmokerStatusNever smoked   -54.72292   42.84052  -1.277   0.2031  
Med.Statin.LLDyes          -11.47693   32.54376  -0.353   0.7247  
Med.all.antiplateletyes    -44.43714   50.67578  -0.877   0.3817  
GFR_MDRD                    -0.08118    0.77576  -0.105   0.9168  
BMI                         -0.36063    3.63115  -0.099   0.9210  
MedHx_CVDNo                -16.42362   29.66718  -0.554   0.5805  
stenose50-70%               76.71781  229.97245   0.334   0.7391  
stenose70-90%              120.40380  199.09274   0.605   0.5461  
stenose90-99%              121.67135  198.86903   0.612   0.5414  
stenose100% (Occlusion)     61.19972  249.31790   0.245   0.8064  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 192.6 on 184 degrees of freedom
Multiple R-squared:  0.051, Adjusted R-squared:  -0.03668 
F-statistic: 0.5816 on 17 and 184 DF,  p-value: 0.9028

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL13_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL13_rank 
Effect size...............: 25.38908 
Standard error............: 14.49443 
Odds ratio (effect size)..: 106251900805 
Lower 95% CI..............: 0.049 
Upper 95% CI..............: 2.313369e+23 
T-value...................: 1.751644 
P-value...................: 0.08150119 
R^2.......................: 0.050997 
Adjusted r^2..............: -0.036682 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing IL21_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -549.48717            19.99262             0.05535  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-244.11 -107.14  -57.03   19.51  890.57 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -813.74393  563.14353  -1.445   0.1502  
currentDF[, TRAIT]          24.30767   14.38887   1.689   0.0928 .
Age                          0.95641    1.98423   0.482   0.6304  
Gendermale                 -15.81199   33.64066  -0.470   0.6389  
ORdate_epoch                 0.06924    0.03727   1.858   0.0648 .
Hypertension.compositeyes   15.95207   43.73838   0.365   0.7157  
DiabetesStatusDiabetes     -17.94458   36.25035  -0.495   0.6212  
SmokerStatusEx-smoker      -16.69040   32.55548  -0.513   0.6088  
SmokerStatusNever smoked   -52.51493   42.67713  -1.231   0.2201  
Med.Statin.LLDyes          -12.29014   32.61252  -0.377   0.7067  
Med.all.antiplateletyes    -45.55642   50.73532  -0.898   0.3704  
GFR_MDRD                    -0.12784    0.77564  -0.165   0.8693  
BMI                         -0.52314    3.63018  -0.144   0.8856  
MedHx_CVDNo                -17.09252   29.66573  -0.576   0.5652  
stenose50-70%               83.20392  229.83310   0.362   0.7178  
stenose70-90%              123.64240  199.11105   0.621   0.5354  
stenose90-99%              124.73424  198.87659   0.627   0.5313  
stenose100% (Occlusion)     52.96675  249.50083   0.212   0.8321  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 192.7 on 184 degrees of freedom
Multiple R-squared:  0.04991,   Adjusted R-squared:  -0.03787 
F-statistic: 0.5686 on 17 and 184 DF,  p-value: 0.9118

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IL21_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IL21_rank 
Effect size...............: 24.30767 
Standard error............: 14.38887 
Odds ratio (effect size)..: 36031759894 
Lower 95% CI..............: 0.02 
Upper 95% CI..............: 6.378754e+22 
T-value...................: 1.689339 
P-value...................: 0.0928483 
R^2.......................: 0.049909 
Adjusted r^2..............: -0.037872 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing INFG_rank
filter: removed 468 rows (75%), 154 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
   -934.2282        0.0871  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-207.59 -119.59  -55.32   29.56  887.61 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.130e+03  7.301e+02  -1.548   0.1239  
currentDF[, TRAIT]         2.254e+00  1.890e+01   0.119   0.9053  
Age                       -8.773e-01  2.407e+00  -0.365   0.7160  
Gendermale                -1.103e+01  4.392e+01  -0.251   0.8022  
ORdate_epoch               1.056e-01  4.939e-02   2.137   0.0344 *
Hypertension.compositeyes  2.758e+00  5.818e+01   0.047   0.9623  
DiabetesStatusDiabetes    -2.604e+01  4.418e+01  -0.589   0.5565  
SmokerStatusEx-smoker      1.009e+01  3.979e+01   0.254   0.8002  
SmokerStatusNever smoked  -3.233e+01  5.409e+01  -0.598   0.5510  
Med.Statin.LLDyes         -2.913e+01  3.933e+01  -0.741   0.4602  
Med.all.antiplateletyes   -5.108e+00  5.765e+01  -0.089   0.9295  
GFR_MDRD                  -5.285e-01  9.724e-01  -0.544   0.5876  
BMI                       -2.314e+00  4.492e+00  -0.515   0.6074  
MedHx_CVDNo               -3.591e+01  3.792e+01  -0.947   0.3453  
stenose50-70%              1.378e+02  2.434e+02   0.566   0.5722  
stenose70-90%              1.745e+02  2.124e+02   0.822   0.4128  
stenose90-99%              1.754e+02  2.112e+02   0.830   0.4077  
stenose100% (Occlusion)    1.188e+02  3.002e+02   0.396   0.6929  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 202.6 on 136 degrees of freedom
Multiple R-squared:  0.05546,   Adjusted R-squared:  -0.0626 
F-statistic: 0.4698 on 17 and 136 DF,  p-value: 0.9625

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' INFG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: INFG_rank 
Effect size...............: 2.253633 
Standard error............: 18.90395 
Odds ratio (effect size)..: 9.522 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.175199e+17 
T-value...................: 0.119215 
P-value...................: 0.905281 
R^2.......................: 0.055462 
Adjusted r^2..............: -0.062605 
Sample size of AE DB......: 622 
Sample size of model......: 154 
Missing data %............: 75.24116 

- processing TNFA_rank
filter: removed 477 rows (77%), 145 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch + MedHx_CVD, 
    data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch   MedHx_CVDNo  
   -967.0629        0.0914      -59.6982  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-221.73 -117.61  -53.31   52.69  823.85 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -9.084e+02  7.288e+02  -1.246   0.2149  
currentDF[, TRAIT]        -2.753e+01  1.833e+01  -1.502   0.1356  
Age                        1.748e-04  2.476e+00   0.000   0.9999  
Gendermale                -1.527e+01  4.325e+01  -0.353   0.7246  
ORdate_epoch               9.330e-02  5.079e-02   1.837   0.0685 .
Hypertension.compositeyes  2.746e+01  5.796e+01   0.474   0.6365  
DiabetesStatusDiabetes    -5.760e-01  4.680e+01  -0.012   0.9902  
SmokerStatusEx-smoker      7.410e+00  4.035e+01   0.184   0.8546  
SmokerStatusNever smoked  -6.431e+01  5.589e+01  -1.151   0.2520  
Med.Statin.LLDyes         -4.758e+01  4.157e+01  -1.144   0.2546  
Med.all.antiplateletyes   -1.585e+01  6.239e+01  -0.254   0.7999  
GFR_MDRD                   1.046e-01  1.075e+00   0.097   0.9226  
BMI                       -4.790e+00  5.324e+00  -0.900   0.3699  
MedHx_CVDNo               -6.784e+01  3.913e+01  -1.734   0.0854 .
stenose70-90%              8.055e+01  1.546e+02   0.521   0.6032  
stenose90-99%              8.474e+01  1.530e+02   0.554   0.5807  
stenose100% (Occlusion)    2.481e+01  2.696e+02   0.092   0.9268  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 204.4 on 128 degrees of freedom
Multiple R-squared:  0.08146,   Adjusted R-squared:  -0.03336 
F-statistic: 0.7095 on 16 and 128 DF,  p-value: 0.7803

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TNFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: TNFA_rank 
Effect size...............: -27.52585 
Standard error............: 18.32789 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 4432.909 
T-value...................: -1.501856 
P-value...................: 0.1355973 
R^2.......................: 0.081458 
Adjusted r^2..............: -0.03336 
Sample size of AE DB......: 622 
Sample size of model......: 145 
Missing data %............: 76.6881 

- processing MIF_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -595.42666       0.05908  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-210.27 -106.97  -60.02   23.95  905.44 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.037e+03  6.132e+02  -1.691   0.0925 .
currentDF[, TRAIT]         1.108e+01  1.679e+01   0.660   0.5099  
Age                        5.325e-01  1.979e+00   0.269   0.7881  
Gendermale                -1.038e+01  3.368e+01  -0.308   0.7582  
ORdate_epoch               8.714e-02  4.196e-02   2.077   0.0392 *
Hypertension.compositeyes  1.588e+01  4.428e+01   0.359   0.7202  
DiabetesStatusDiabetes    -2.090e+01  3.647e+01  -0.573   0.5673  
SmokerStatusEx-smoker     -8.847e+00  3.264e+01  -0.271   0.7867  
SmokerStatusNever smoked  -4.247e+01  4.248e+01  -1.000   0.3188  
Med.Statin.LLDyes         -8.048e+00  3.271e+01  -0.246   0.8059  
Med.all.antiplateletyes   -4.421e+01  5.119e+01  -0.864   0.3889  
GFR_MDRD                  -4.513e-02  7.922e-01  -0.057   0.9546  
BMI                       -7.256e-01  3.655e+00  -0.199   0.8428  
MedHx_CVDNo               -1.841e+01  2.988e+01  -0.616   0.5385  
stenose50-70%              1.043e+02  2.311e+02   0.451   0.6523  
stenose70-90%              1.341e+02  2.007e+02   0.668   0.5050  
stenose90-99%              1.432e+02  2.003e+02   0.715   0.4757  
stenose100% (Occlusion)    5.252e+01  2.514e+02   0.209   0.8347  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194 on 184 degrees of freedom
Multiple R-squared:  0.03745,   Adjusted R-squared:  -0.05148 
F-statistic: 0.4211 on 17 and 184 DF,  p-value: 0.9792

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIF_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MIF_rank 
Effect size...............: 11.08372 
Standard error............: 16.78661 
Odds ratio (effect size)..: 65102.61 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.266626e+19 
T-value...................: 0.660272 
P-value...................: 0.5099053 
R^2.......................: 0.037453 
Adjusted r^2..............: -0.051478 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MCP1_rank
filter: removed 422 rows (68%), 200 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -579.67380       0.05789  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-216.43 -104.79  -55.81   17.15  914.36 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -959.34496  583.55474  -1.644   0.1019  
currentDF[, TRAIT]           8.99600   14.36496   0.626   0.5319  
Age                          0.81366    2.00538   0.406   0.6854  
Gendermale                 -13.59195   34.19475  -0.397   0.6915  
ORdate_epoch                 0.07985    0.03862   2.068   0.0401 *
Hypertension.compositeyes   18.57258   44.46921   0.418   0.6767  
DiabetesStatusDiabetes     -22.53319   36.68397  -0.614   0.5398  
SmokerStatusEx-smoker      -11.29593   32.73943  -0.345   0.7305  
SmokerStatusNever smoked   -45.95087   42.88098  -1.072   0.2853  
Med.Statin.LLDyes           -6.94201   32.95857  -0.211   0.8334  
Med.all.antiplateletyes    -63.01939   54.29691  -1.161   0.2473  
GFR_MDRD                    -0.12223    0.78637  -0.155   0.8767  
BMI                         -0.32909    3.72199  -0.088   0.9296  
MedHx_CVDNo                -19.17199   30.10813  -0.637   0.5251  
stenose50-70%              115.28600  231.12232   0.499   0.6185  
stenose70-90%              142.04894  200.50951   0.708   0.4796  
stenose90-99%              154.26184  199.80843   0.772   0.4411  
stenose100% (Occlusion)     49.42882  252.46420   0.196   0.8450  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.4 on 182 degrees of freedom
Multiple R-squared:  0.04097,   Adjusted R-squared:  -0.04861 
F-statistic: 0.4574 on 17 and 182 DF,  p-value: 0.9681

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MCP1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MCP1_rank 
Effect size...............: 8.996001 
Standard error............: 14.36496 
Odds ratio (effect size)..: 8070.746 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.363379e+16 
T-value...................: 0.626246 
P-value...................: 0.5319382 
R^2.......................: 0.040972 
Adjusted r^2..............: -0.048607 
Sample size of AE DB......: 622 
Sample size of model......: 200 
Missing data %............: 67.84566 

- processing MIP1a_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -549.03607            20.72285             0.05538  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-238.26 -107.43  -56.34   30.35  901.59 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -816.41951  578.17760  -1.412   0.1598  
currentDF[, TRAIT]          21.12199   14.84396   1.423   0.1566  
Age                          0.50235    2.05756   0.244   0.8074  
Gendermale                  -9.40047   35.55001  -0.264   0.7918  
ORdate_epoch                 0.06917    0.03815   1.813   0.0716 .
Hypertension.compositeyes   29.73900   46.56903   0.639   0.5239  
DiabetesStatusDiabetes     -20.42486   38.39172  -0.532   0.5954  
SmokerStatusEx-smoker      -13.20276   34.40457  -0.384   0.7016  
SmokerStatusNever smoked   -48.30374   44.20580  -1.093   0.2761  
Med.Statin.LLDyes           -3.67667   34.34110  -0.107   0.9149  
Med.all.antiplateletyes    -36.34477   57.64677  -0.630   0.5292  
GFR_MDRD                     0.05182    0.80267   0.065   0.9486  
BMI                         -0.93470    3.80107  -0.246   0.8061  
MedHx_CVDNo                -25.06317   31.87635  -0.786   0.4328  
stenose50-70%               87.37067  249.99444   0.349   0.7272  
stenose70-90%              124.33135  202.43618   0.614   0.5399  
stenose90-99%              129.86983  202.10686   0.643   0.5214  
stenose100% (Occlusion)     54.07043  254.89493   0.212   0.8323  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 195.2 on 171 degrees of freedom
Multiple R-squared:  0.0482,    Adjusted R-squared:  -0.04643 
F-statistic: 0.5093 on 17 and 171 DF,  p-value: 0.946

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIP1a_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MIP1a_rank 
Effect size...............: 21.12198 
Standard error............: 14.84396 
Odds ratio (effect size)..: 1489915331 
Lower 95% CI..............: 0 
Upper 95% CI..............: 6.435722e+21 
T-value...................: 1.422934 
P-value...................: 0.1565769 
R^2.......................: 0.048195 
Adjusted r^2..............: -0.046428 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing RANTES_rank
filter: removed 424 rows (68%), 198 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -583.35357       0.05819  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-229.60 -109.76  -61.13   28.75  923.72 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.147e+03  6.068e+02  -1.890   0.0604 .
currentDF[, TRAIT]         2.338e+01  1.592e+01   1.468   0.1438  
Age                        1.037e+00  2.026e+00   0.512   0.6095  
Gendermale                -1.738e+01  3.467e+01  -0.501   0.6167  
ORdate_epoch               9.601e-02  4.091e-02   2.347   0.0200 *
Hypertension.compositeyes  1.834e+01  4.598e+01   0.399   0.6905  
DiabetesStatusDiabetes    -1.187e+01  3.748e+01  -0.317   0.7519  
SmokerStatusEx-smoker     -6.413e+00  3.344e+01  -0.192   0.8482  
SmokerStatusNever smoked  -4.695e+01  4.325e+01  -1.086   0.2791  
Med.Statin.LLDyes         -6.740e+00  3.318e+01  -0.203   0.8393  
Med.all.antiplateletyes   -5.342e+01  5.412e+01  -0.987   0.3250  
GFR_MDRD                  -1.706e-02  7.909e-01  -0.022   0.9828  
BMI                       -1.374e+00  3.736e+00  -0.368   0.7135  
MedHx_CVDNo               -1.069e+01  3.102e+01  -0.345   0.7308  
stenose50-70%              1.100e+02  2.475e+02   0.445   0.6572  
stenose70-90%              1.170e+02  2.019e+02   0.579   0.5630  
stenose90-99%              1.312e+02  2.009e+02   0.653   0.5147  
stenose100% (Occlusion)    1.434e+01  2.547e+02   0.056   0.9551  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.9 on 180 degrees of freedom
Multiple R-squared:  0.04709,   Adjusted R-squared:  -0.0429 
F-statistic: 0.5233 on 17 and 180 DF,  p-value: 0.939

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' RANTES_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: RANTES_rank 
Effect size...............: 23.3762 
Standard error............: 15.92046 
Odds ratio (effect size)..: 14195656897 
Lower 95% CI..............: 0 
Upper 95% CI..............: 5.057374e+23 
T-value...................: 1.468312 
P-value...................: 0.1437648 
R^2.......................: 0.047092 
Adjusted r^2..............: -0.042905 
Sample size of AE DB......: 622 
Sample size of model......: 198 
Missing data %............: 68.1672 

- processing MIG_rank
filter: removed 423 rows (68%), 199 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
            143.24               33.68  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-273.62 -104.85  -52.65   21.30  886.05 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -529.41864  581.78088  -0.910   0.3640  
currentDF[, TRAIT]          36.35714   15.64862   2.323   0.0213 *
Age                          1.26423    1.99470   0.634   0.5270  
Gendermale                 -20.84024   33.85373  -0.616   0.5389  
ORdate_epoch                 0.04692    0.03913   1.199   0.2320  
Hypertension.compositeyes   13.53763   44.88270   0.302   0.7633  
DiabetesStatusDiabetes     -22.39565   36.71506  -0.610   0.5426  
SmokerStatusEx-smoker      -23.11434   32.84319  -0.704   0.4825  
SmokerStatusNever smoked   -58.41474   42.97676  -1.359   0.1758  
Med.Statin.LLDyes           -9.08412   32.84828  -0.277   0.7824  
Med.all.antiplateletyes    -49.73774   51.63087  -0.963   0.3367  
GFR_MDRD                    -0.26978    0.77573  -0.348   0.7284  
BMI                         -0.59644    3.62277  -0.165   0.8694  
MedHx_CVDNo                -19.21883   30.06850  -0.639   0.5235  
stenose50-70%               78.20209  245.23646   0.319   0.7502  
stenose70-90%              122.95937  198.34934   0.620   0.5361  
stenose90-99%              121.73267  197.89572   0.615   0.5392  
stenose100% (Occlusion)     44.82830  249.20861   0.180   0.8574  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 191.9 on 181 degrees of freedom
Multiple R-squared:  0.06574,   Adjusted R-squared:  -0.02201 
F-statistic: 0.7492 on 17 and 181 DF,  p-value: 0.7487

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MIG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MIG_rank 
Effect size...............: 36.35714 
Standard error............: 15.64862 
Odds ratio (effect size)..: 6.161764e+15 
Lower 95% CI..............: 294.665 
Upper 95% CI..............: 1.288491e+29 
T-value...................: 2.323344 
P-value...................: 0.02127203 
R^2.......................: 0.065742 
Adjusted r^2..............: -0.022006 
Sample size of AE DB......: 622 
Sample size of model......: 199 
Missing data %............: 68.00643 

- processing IP10_rank
filter: removed 439 rows (71%), 183 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
         -680.4241             24.9911              0.0659  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-247.73  -98.16  -52.71   30.53  889.41 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.020e+03  5.864e+02  -1.740   0.0838 .
currentDF[, TRAIT]         2.973e+01  1.570e+01   1.894   0.0600 .
Age                        8.596e-01  2.143e+00   0.401   0.6888  
Gendermale                 3.446e+00  3.517e+01   0.098   0.9221  
ORdate_epoch               8.172e-02  3.858e-02   2.118   0.0356 *
Hypertension.compositeyes  3.785e+01  4.762e+01   0.795   0.4279  
DiabetesStatusDiabetes    -2.617e+01  3.990e+01  -0.656   0.5128  
SmokerStatusEx-smoker     -2.075e+01  3.550e+01  -0.584   0.5598  
SmokerStatusNever smoked  -4.930e+01  4.573e+01  -1.078   0.2825  
Med.Statin.LLDyes          4.238e+00  3.520e+01   0.120   0.9043  
Med.all.antiplateletyes   -2.227e+01  5.452e+01  -0.408   0.6835  
GFR_MDRD                  -2.714e-02  8.371e-01  -0.032   0.9742  
BMI                       -1.448e+00  3.791e+00  -0.382   0.7030  
MedHx_CVDNo               -3.487e+01  3.222e+01  -1.082   0.2808  
stenose50-70%              1.009e+02  2.507e+02   0.402   0.6879  
stenose70-90%              1.355e+02  2.034e+02   0.666   0.5062  
stenose90-99%              1.452e+02  2.023e+02   0.718   0.4740  
stenose100% (Occlusion)    1.146e+02  2.555e+02   0.448   0.6545  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 196 on 165 degrees of freedom
Multiple R-squared:  0.06603,   Adjusted R-squared:  -0.0302 
F-statistic: 0.6862 on 17 and 165 DF,  p-value: 0.8135

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' IP10_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: IP10_rank 
Effect size...............: 29.72744 
Standard error............: 15.69949 
Odds ratio (effect size)..: 8.136947e+12 
Lower 95% CI..............: 0.352 
Upper 95% CI..............: 1.879912e+26 
T-value...................: 1.893529 
P-value...................: 0.0600379 
R^2.......................: 0.066029 
Adjusted r^2..............: -0.030199 
Sample size of AE DB......: 622 
Sample size of model......: 183 
Missing data %............: 70.57878 

- processing Eotaxin1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -595.42666       0.05908  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-225.60 -106.48  -59.53   24.37  909.93 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -769.00897  571.49265  -1.346   0.1801  
currentDF[, TRAIT]          17.57292   14.65565   1.199   0.2320  
Age                          0.75068    1.98529   0.378   0.7058  
Gendermale                 -14.39509   33.81495  -0.426   0.6708  
ORdate_epoch                 0.06588    0.03798   1.735   0.0845 .
Hypertension.compositeyes   12.28269   43.85703   0.280   0.7797  
DiabetesStatusDiabetes     -19.19468   36.39179  -0.527   0.5985  
SmokerStatusEx-smoker      -14.38968   32.63517  -0.441   0.6598  
SmokerStatusNever smoked   -48.57383   42.80144  -1.135   0.2579  
Med.Statin.LLDyes           -9.34473   32.64418  -0.286   0.7750  
Med.all.antiplateletyes    -44.94931   50.95582  -0.882   0.3789  
GFR_MDRD                    -0.11253    0.77879  -0.144   0.8853  
BMI                         -0.48609    3.64590  -0.133   0.8941  
MedHx_CVDNo                -17.41960   29.79750  -0.585   0.5595  
stenose50-70%               96.84701  230.43388   0.420   0.6748  
stenose70-90%              129.74264  199.88127   0.649   0.5171  
stenose90-99%              133.51770  199.66989   0.669   0.5045  
stenose100% (Occlusion)     46.59851  250.68092   0.186   0.8527  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 193.4 on 184 degrees of freedom
Multiple R-squared:  0.04265,   Adjusted R-squared:  -0.0458 
F-statistic: 0.4822 on 17 and 184 DF,  p-value: 0.9586

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' Eotaxin1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: Eotaxin1_rank 
Effect size...............: 17.57292 
Standard error............: 14.65565 
Odds ratio (effect size)..: 42837496 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.27927e+20 
T-value...................: 1.199055 
P-value...................: 0.2320485 
R^2.......................: 0.042653 
Adjusted r^2..............: -0.045797 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing TARC_rank
filter: removed 444 rows (71%), 178 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
      154.1  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-262.76 -109.02  -59.47   31.29  898.19 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.067e+03  7.149e+02  -1.492   0.1377  
currentDF[, TRAIT]         2.768e+01  1.712e+01   1.617   0.1079  
Age                        1.568e+00  2.264e+00   0.692   0.4897  
Gendermale                -8.352e+00  3.817e+01  -0.219   0.8271  
ORdate_epoch               8.345e-02  4.959e-02   1.683   0.0944 .
Hypertension.compositeyes  2.264e+01  4.811e+01   0.471   0.6386  
DiabetesStatusDiabetes    -2.275e+01  4.027e+01  -0.565   0.5729  
SmokerStatusEx-smoker     -1.347e+01  3.654e+01  -0.369   0.7129  
SmokerStatusNever smoked  -5.601e+01  4.669e+01  -1.200   0.2320  
Med.Statin.LLDyes         -1.946e+01  3.800e+01  -0.512   0.6092  
Med.all.antiplateletyes   -4.822e+01  5.889e+01  -0.819   0.4141  
GFR_MDRD                   1.206e-01  8.818e-01   0.137   0.8914  
BMI                       -6.812e-01  4.072e+00  -0.167   0.8673  
MedHx_CVDNo               -2.016e+01  3.353e+01  -0.601   0.5485  
stenose50-70%              8.714e+01  2.575e+02   0.338   0.7355  
stenose70-90%              1.520e+02  2.104e+02   0.723   0.4710  
stenose90-99%              1.432e+02  2.097e+02   0.683   0.4957  
stenose100% (Occlusion)    6.627e+01  2.670e+02   0.248   0.8043  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 203.3 on 160 degrees of freedom
Multiple R-squared:  0.04682,   Adjusted R-squared:  -0.05446 
F-statistic: 0.4623 on 17 and 160 DF,  p-value: 0.966

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: TARC_rank 
Effect size...............: 27.67735 
Standard error............: 17.11956 
Odds ratio (effect size)..: 1.047421e+12 
Lower 95% CI..............: 0.003 
Upper 95% CI..............: 3.913691e+26 
T-value...................: 1.616709 
P-value...................: 0.107911 
R^2.......................: 0.046816 
Adjusted r^2..............: -0.05446 
Sample size of AE DB......: 622 
Sample size of model......: 178 
Missing data %............: 71.38264 

- processing PARC_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -839.60679            20.70565             0.07849  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-224.93 -103.98  -55.73   21.66  877.98 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.236e+03  6.081e+02  -2.033   0.0435 *
currentDF[, TRAIT]         2.383e+01  1.547e+01   1.540   0.1252  
Age                        6.982e-01  1.970e+00   0.354   0.7234  
Gendermale                -7.589e+00  3.348e+01  -0.227   0.8209  
ORdate_epoch               9.871e-02  4.036e-02   2.446   0.0154 *
Hypertension.compositeyes  2.303e+01  4.428e+01   0.520   0.6036  
DiabetesStatusDiabetes    -1.893e+01  3.627e+01  -0.522   0.6023  
SmokerStatusEx-smoker     -1.239e+01  3.241e+01  -0.382   0.7027  
SmokerStatusNever smoked  -4.993e+01  4.257e+01  -1.173   0.2424  
Med.Statin.LLDyes         -9.246e+00  3.254e+01  -0.284   0.7766  
Med.all.antiplateletyes   -3.661e+01  5.083e+01  -0.720   0.4723  
GFR_MDRD                   2.138e-02  7.831e-01   0.027   0.9782  
BMI                       -7.219e-02  3.654e+00  -0.020   0.9843  
MedHx_CVDNo               -2.125e+01  2.968e+01  -0.716   0.4751  
stenose50-70%              1.151e+02  2.293e+02   0.502   0.6164  
stenose70-90%              1.397e+02  1.990e+02   0.702   0.4835  
stenose90-99%              1.541e+02  1.982e+02   0.777   0.4380  
stenose100% (Occlusion)    9.042e+01  2.505e+02   0.361   0.7186  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 193 on 184 degrees of freedom
Multiple R-squared:  0.04746,   Adjusted R-squared:  -0.04055 
F-statistic: 0.5392 on 17 and 184 DF,  p-value: 0.9302

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' PARC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: PARC_rank 
Effect size...............: 23.83311 
Standard error............: 15.4727 
Odds ratio (effect size)..: 22417658328 
Lower 95% CI..............: 0.002 
Upper 95% CI..............: 3.320626e+23 
T-value...................: 1.540333 
P-value...................: 0.1251972 
R^2.......................: 0.047455 
Adjusted r^2..............: -0.040551 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MDC_rank
filter: removed 433 rows (70%), 189 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -783.10023            23.39734             0.07401  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-245.50 -107.44  -51.53   39.01  884.38 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.082e+03  5.946e+02  -1.820   0.0706 .
currentDF[, TRAIT]         2.440e+01  1.563e+01   1.561   0.1204  
Age                        4.990e-01  2.048e+00   0.244   0.8078  
Gendermale                -6.010e+00  3.527e+01  -0.170   0.8649  
ORdate_epoch               8.900e-02  3.969e-02   2.242   0.0262 *
Hypertension.compositeyes  3.108e+01  4.648e+01   0.669   0.5045  
DiabetesStatusDiabetes    -1.683e+01  3.866e+01  -0.435   0.6639  
SmokerStatusEx-smoker     -7.226e+00  3.418e+01  -0.211   0.8328  
SmokerStatusNever smoked  -4.683e+01  4.401e+01  -1.064   0.2888  
Med.Statin.LLDyes         -9.263e+00  3.468e+01  -0.267   0.7897  
Med.all.antiplateletyes   -2.854e+01  5.770e+01  -0.495   0.6215  
GFR_MDRD                   7.812e-02  7.983e-01   0.098   0.9222  
BMI                       -9.612e-01  3.814e+00  -0.252   0.8013  
MedHx_CVDNo               -2.471e+01  3.182e+01  -0.777   0.4385  
stenose50-70%              8.087e+01  2.498e+02   0.324   0.7465  
stenose70-90%              1.273e+02  2.018e+02   0.631   0.5290  
stenose90-99%              1.357e+02  2.011e+02   0.675   0.5008  
stenose100% (Occlusion)    6.229e+01  2.543e+02   0.245   0.8068  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.9 on 171 degrees of freedom
Multiple R-squared:  0.04959,   Adjusted R-squared:  -0.04489 
F-statistic: 0.5249 on 17 and 171 DF,  p-value: 0.9379

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MDC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MDC_rank 
Effect size...............: 24.39727 
Standard error............: 15.62908 
Odds ratio (effect size)..: 39409527322 
Lower 95% CI..............: 0.002 
Upper 95% CI..............: 7.931194e+23 
T-value...................: 1.561018 
P-value...................: 0.1203682 
R^2.......................: 0.049592 
Adjusted r^2..............: -0.044893 
Sample size of AE DB......: 622 
Sample size of model......: 189 
Missing data %............: 69.61415 

- processing OPG_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -595.42666       0.05908  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-204.96 -105.84  -57.32   15.11  919.96 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -898.00441  566.83367  -1.584   0.1149  
currentDF[, TRAIT]           7.06770   14.17536   0.499   0.6187  
Age                          0.59262    2.00158   0.296   0.7675  
Gendermale                 -10.32970   33.71851  -0.306   0.7597  
ORdate_epoch                 0.07493    0.03741   2.003   0.0467 *
Hypertension.compositeyes   14.36349   44.15906   0.325   0.7453  
DiabetesStatusDiabetes     -22.00132   36.41811  -0.604   0.5465  
SmokerStatusEx-smoker      -12.70657   32.89677  -0.386   0.6998  
SmokerStatusNever smoked   -42.63938   42.69875  -0.999   0.3193  
Med.Statin.LLDyes           -7.82172   32.73196  -0.239   0.8114  
Med.all.antiplateletyes    -43.72500   51.25315  -0.853   0.3947  
GFR_MDRD                    -0.13372    0.78109  -0.171   0.8643  
BMI                         -0.49530    3.66895  -0.135   0.8928  
MedHx_CVDNo                -19.70630   29.83714  -0.660   0.5098  
stenose50-70%              112.40078  230.73343   0.487   0.6267  
stenose70-90%              146.89802  200.10420   0.734   0.4638  
stenose90-99%              156.87099  199.36270   0.787   0.4324  
stenose100% (Occlusion)     70.41466  251.99829   0.279   0.7802  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.1 on 184 degrees of freedom
Multiple R-squared:  0.03647,   Adjusted R-squared:  -0.05255 
F-statistic: 0.4097 on 17 and 184 DF,  p-value: 0.982

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' OPG_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: OPG_rank 
Effect size...............: 7.0677 
Standard error............: 14.17536 
Odds ratio (effect size)..: 1173.446 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.367017e+15 
T-value...................: 0.498591 
P-value...................: 0.6186634 
R^2.......................: 0.036474 
Adjusted r^2..............: -0.052547 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing sICAM1_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -595.42666       0.05908  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-208.90 -107.12  -59.45   18.29  922.87 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -874.98462  581.37306  -1.505   0.1340  
currentDF[, TRAIT]          -0.59945   14.91174  -0.040   0.9680  
Age                          0.40800    2.01473   0.203   0.8397  
Gendermale                  -9.24000   33.69708  -0.274   0.7842  
ORdate_epoch                 0.07417    0.03862   1.920   0.0564 .
Hypertension.compositeyes   12.29809   44.27175   0.278   0.7815  
DiabetesStatusDiabetes     -22.58646   36.53333  -0.618   0.5372  
SmokerStatusEx-smoker      -10.34652   32.61779  -0.317   0.7514  
SmokerStatusNever smoked   -39.42007   42.98150  -0.917   0.3603  
Med.Statin.LLDyes           -6.97670   32.71859  -0.213   0.8314  
Med.all.antiplateletyes    -41.27648   51.09743  -0.808   0.4202  
GFR_MDRD                    -0.13685    0.78532  -0.174   0.8618  
BMI                         -0.65089    3.65892  -0.178   0.8590  
MedHx_CVDNo                -19.75893   30.05048  -0.658   0.5117  
stenose50-70%              115.72191  231.04859   0.501   0.6171  
stenose70-90%              146.34915  201.12651   0.728   0.4678  
stenose90-99%              157.54215  200.31835   0.786   0.4326  
stenose100% (Occlusion)     61.11278  251.79656   0.243   0.8085  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 194.2 on 184 degrees of freedom
Multiple R-squared:  0.03518,   Adjusted R-squared:  -0.05396 
F-statistic: 0.3947 on 17 and 184 DF,  p-value: 0.9853

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' sICAM1_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: sICAM1_rank 
Effect size...............: -0.599451 
Standard error............: 14.91174 
Odds ratio (effect size)..: 0.549 
Lower 95% CI..............: 0 
Upper 95% CI..............: 2.708875e+12 
T-value...................: -0.0402 
P-value...................: 0.9679773 
R^2.......................: 0.035181 
Adjusted r^2..............: -0.05396 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing VEGFA_rank
filter: removed 445 rows (72%), 177 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -761.88307       0.07303  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-211.87 -112.75  -62.08   37.07  951.47 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -857.05064  674.67248  -1.270   0.2058  
currentDF[, TRAIT]          -7.51938   18.06444  -0.416   0.6778  
Age                          0.65319    2.36424   0.276   0.7827  
Gendermale                 -24.22377   40.08486  -0.604   0.5465  
ORdate_epoch                 0.08736    0.05015   1.742   0.0834 .
Hypertension.compositeyes   15.89088   53.55795   0.297   0.7671  
DiabetesStatusDiabetes     -20.29456   43.00537  -0.472   0.6376  
SmokerStatusEx-smoker       -6.47541   38.23480  -0.169   0.8657  
SmokerStatusNever smoked   -47.34103   52.36518  -0.904   0.3673  
Med.Statin.LLDyes           -3.99001   38.43545  -0.104   0.9174  
Med.all.antiplateletyes    -31.51279   57.40857  -0.549   0.5838  
GFR_MDRD                    -0.07063    0.84980  -0.083   0.9339  
BMI                         -3.13452    4.39684  -0.713   0.4769  
MedHx_CVDNo                 -8.99507   35.69762  -0.252   0.8014  
stenose70-90%               21.24933  164.67280   0.129   0.8975  
stenose90-99%               11.19314  163.13774   0.069   0.9454  
stenose100% (Occlusion)    -89.30165  232.24506  -0.385   0.7011  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 214.7 on 160 degrees of freedom
Multiple R-squared:  0.03975,   Adjusted R-squared:  -0.05628 
F-statistic: 0.4139 on 16 and 160 DF,  p-value: 0.9774

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' VEGFA_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: VEGFA_rank 
Effect size...............: -7.51938 
Standard error............: 18.06444 
Odds ratio (effect size)..: 0.001 
Lower 95% CI..............: 0 
Upper 95% CI..............: 1.291638e+12 
T-value...................: -0.416253 
P-value...................: 0.6777827 
R^2.......................: 0.039748 
Adjusted r^2..............: -0.056277 
Sample size of AE DB......: 622 
Sample size of model......: 177 
Missing data %............: 71.54341 

- processing TGFB_rank
filter: removed 419 rows (67%), 203 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -831.77203       0.07845  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-232.22 -106.11  -60.38   34.46  924.97 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.162e+03  5.947e+02  -1.954   0.0522 .
currentDF[, TRAIT]         1.841e+01  1.510e+01   1.220   0.2240  
Age                        9.471e-01  2.109e+00   0.449   0.6539  
Gendermale                -2.174e+01  3.538e+01  -0.614   0.5397  
ORdate_epoch               9.468e-02  3.950e-02   2.397   0.0175 *
Hypertension.compositeyes  3.227e+01  4.599e+01   0.702   0.4838  
DiabetesStatusDiabetes    -2.667e+01  3.750e+01  -0.711   0.4778  
SmokerStatusEx-smoker     -1.043e+01  3.445e+01  -0.303   0.7625  
SmokerStatusNever smoked  -5.163e+01  4.570e+01  -1.130   0.2600  
Med.Statin.LLDyes          1.823e+00  3.457e+01   0.053   0.9580  
Med.all.antiplateletyes   -4.266e+01  5.557e+01  -0.768   0.4436  
GFR_MDRD                   2.633e-01  8.244e-01   0.319   0.7498  
BMI                       -1.775e+00  3.901e+00  -0.455   0.6497  
MedHx_CVDNo               -7.356e+00  3.155e+01  -0.233   0.8159  
stenose50-70%              8.851e+01  2.456e+02   0.360   0.7190  
stenose70-90%              1.395e+02  2.121e+02   0.658   0.5117  
stenose90-99%              1.414e+02  2.114e+02   0.669   0.5044  
stenose100% (Occlusion)    1.853e+01  2.682e+02   0.069   0.9450  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 205.3 on 185 degrees of freedom
Multiple R-squared:  0.05637,   Adjusted R-squared:  -0.03034 
F-statistic: 0.6501 on 17 and 185 DF,  p-value: 0.8479

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' TGFB_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: TGFB_rank 
Effect size...............: 18.415 
Standard error............: 15.09506 
Odds ratio (effect size)..: 99433083 
Lower 95% CI..............: 0 
Upper 95% CI..............: 7.025917e+20 
T-value...................: 1.219935 
P-value...................: 0.2240422 
R^2.......................: 0.056371 
Adjusted r^2..............: -0.030341 
Sample size of AE DB......: 622 
Sample size of model......: 203 
Missing data %............: 67.36334 

- processing MMP2_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -588.25470       0.05846  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-211.23 -103.08  -58.52   34.10  912.74 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)
(Intercept)               -585.87301  570.97658  -1.026    0.306
currentDF[, TRAIT]         -18.69568   14.57490  -1.283    0.201
Age                         -0.53589    1.94360  -0.276    0.783
Gendermale                 -17.27846   32.87773  -0.526    0.600
ORdate_epoch                 0.06212    0.03769   1.648    0.101
Hypertension.compositeyes   -8.19819   44.45142  -0.184    0.854
DiabetesStatusDiabetes     -19.65852   35.59715  -0.552    0.581
SmokerStatusEx-smoker        7.65630   32.20298   0.238    0.812
SmokerStatusNever smoked   -17.56917   42.52024  -0.413    0.680
Med.Statin.LLDyes           -6.02004   31.98231  -0.188    0.851
Med.all.antiplateletyes    -39.18601   50.21618  -0.780    0.436
GFR_MDRD                    -0.63804    0.74505  -0.856    0.393
BMI                         -1.66937    3.70511  -0.451    0.653
MedHx_CVDNo                -23.53385   29.49536  -0.798    0.426
stenose50-70%              131.19782  227.35079   0.577    0.565
stenose70-90%              146.34361  197.36497   0.741    0.459
stenose90-99%              154.96431  196.51619   0.789    0.431
stenose100% (Occlusion)     46.28176  247.92739   0.187    0.852

Residual standard error: 191.1 on 184 degrees of freedom
Multiple R-squared:  0.04312,   Adjusted R-squared:  -0.04528 
F-statistic: 0.4878 on 17 and 184 DF,  p-value: 0.9563

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP2_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MMP2_rank 
Effect size...............: -18.69568 
Standard error............: 14.5749 
Odds ratio (effect size)..: 0 
Lower 95% CI..............: 0 
Upper 95% CI..............: 19362.95 
T-value...................: -1.282732 
P-value...................: 0.2011996 
R^2.......................: 0.043123 
Adjusted r^2..............: -0.045284 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP8_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_epoch, data = currentDF)

Coefficients:
 (Intercept)  ORdate_epoch  
  -588.25470       0.05846  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-206.32 -101.69  -55.24   27.81  918.24 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -673.66594  562.53501  -1.198   0.2326  
currentDF[, TRAIT]          15.92602   14.09295   1.130   0.2599  
Age                         -0.14160    1.93974  -0.073   0.9419  
Gendermale                 -18.33864   33.13693  -0.553   0.5806  
ORdate_epoch                 0.06916    0.03713   1.863   0.0641 .
Hypertension.compositeyes    0.77161   43.92616   0.018   0.9860  
DiabetesStatusDiabetes     -15.41899   35.90461  -0.429   0.6681  
SmokerStatusEx-smoker        6.59946   32.19447   0.205   0.8378  
SmokerStatusNever smoked   -24.57053   42.18095  -0.583   0.5609  
Med.Statin.LLDyes           -5.36658   32.01279  -0.168   0.8671  
Med.all.antiplateletyes    -40.49969   50.22443  -0.806   0.4211  
GFR_MDRD                    -0.38323    0.74848  -0.512   0.6093  
BMI                         -1.68055    3.71230  -0.453   0.6513  
MedHx_CVDNo                -19.14639   29.65915  -0.646   0.5194  
stenose50-70%               82.36604  229.21388   0.359   0.7198  
stenose70-90%               89.77532  202.14072   0.444   0.6575  
stenose90-99%              105.21673  200.50132   0.525   0.6004  
stenose100% (Occlusion)     -6.11296  252.61424  -0.024   0.9807  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 191.3 on 184 degrees of freedom
Multiple R-squared:  0.04122,   Adjusted R-squared:  -0.04736 
F-statistic: 0.4653 on 17 and 184 DF,  p-value: 0.9653

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP8_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MMP8_rank 
Effect size...............: 15.92602 
Standard error............: 14.09295 
Odds ratio (effect size)..: 8252462 
Lower 95% CI..............: 0 
Upper 95% CI..............: 8.17984e+18 
T-value...................: 1.13007 
P-value...................: 0.2599176 
R^2.......................: 0.041221 
Adjusted r^2..............: -0.047362 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 

- processing MMP9_rank
filter: removed 420 rows (68%), 202 rows remaining
filter_if: no rows removed

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]        ORdate_epoch  
        -704.49683            24.59556             0.06777  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-215.50 -103.93  -54.74   35.05  903.41 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -854.24450  561.45505  -1.521   0.1299  
currentDF[, TRAIT]          25.04696   13.84364   1.809   0.0720 .
Age                          0.07510    1.93550   0.039   0.9691  
Gendermale                 -15.80068   32.53664  -0.486   0.6278  
ORdate_epoch                 0.07906    0.03718   2.126   0.0348 *
Hypertension.compositeyes   14.51065   44.33455   0.327   0.7438  
DiabetesStatusDiabetes     -12.36375   35.71770  -0.346   0.7296  
SmokerStatusEx-smoker        2.98272   31.98073   0.093   0.9258  
SmokerStatusNever smoked   -30.42857   42.06733  -0.723   0.4704  
Med.Statin.LLDyes           -7.67966   31.86230  -0.241   0.8098  
Med.all.antiplateletyes    -49.13710   49.99691  -0.983   0.3270  
GFR_MDRD                    -0.27443    0.74736  -0.367   0.7139  
BMI                         -1.63472    3.68341  -0.444   0.6577  
MedHx_CVDNo                -17.89552   29.46392  -0.607   0.5444  
stenose50-70%              108.14932  226.09638   0.478   0.6330  
stenose70-90%              121.42317  196.64276   0.617   0.5377  
stenose90-99%              134.67853  195.77927   0.688   0.4924  
stenose100% (Occlusion)     41.68767  246.86505   0.169   0.8661  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 190.2 on 184 degrees of freedom
Multiple R-squared:  0.05144,   Adjusted R-squared:  -0.0362 
F-statistic: 0.587 on 17 and 184 DF,  p-value: 0.899

Analyzing in dataset ' AEDB.CEA ' the association of ' CD36 ' with ' MMP9_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: CD36 
Trait/outcome.............: MMP9_rank 
Effect size...............: 25.04696 
Standard error............: 13.84364 
Odds ratio (effect size)..: 75467110683 
Lower 95% CI..............: 0.124 
Upper 95% CI..............: 4.588854e+22 
T-value...................: 1.809275 
P-value...................: 0.07204037 
R^2.......................: 0.051442 
Adjusted r^2..............: -0.036197 
Sample size of AE DB......: 622 
Sample size of model......: 202 
Missing data %............: 67.52412 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Target expression vs. vulnerability index

Here we plot the levels of inverse-rank normal transformed PCSK9 plaque levels from experiment 1 and 2 to the Plaque vulnerability index.

library(sjlabelled)

AERNASE.clin$yeartemp <- as.numeric(year(AERNASE.clin$dateok))

attach(AERNASE.clin)

AERNASE.clin[,"ORyearGroup"] <- NA
AERNASE.clin$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin)

table(AERNASE.clin$ORyearGroup, AERNASE.clin$ORdate_year)
        
         No data available/missing 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
  < 2007                         0   31   61   66   82   85   67    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  > 2007                         0    0    0    0    0    0    0   63   68   34   31   22    5    3    3    1    0    0    0    0    0    0

Visualisations

PCSK9

# Global test

compare_means(PCSK9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())
Saving 7.29 x 4.51 in image

Model 1

In this model we correct for Age, Gender, and year of surgery.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability index as a function of plaque PCSK9 levels.

TRAITS.TARGET.RANK.extra = c("PCSK9")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, 
    #           Hess = TRUE)
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

Analysis of PCSK9.

- processing Plaque_Vulnerability_Index
filter: no rows removed
filter_if: no rows removed
Warning: NaNs produced
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age + 
    Gender + ORdate_epoch, data = currentDF, Hess = TRUE)

Coefficients:
                          Value Std. Error t value
currentDF[, PROTEIN]  0.1146763  0.0428127   2.679
Age                   0.0147499  0.0004045  36.465
Gendermale            0.5792864  0.0031785 182.253
ORdate_epoch         -0.0004569        NaN     NaN

Intercepts:
    Value      Std. Error t value   
0|1    -7.2796     0.0009 -8070.1471
1|2    -5.8208     0.0189  -307.9476
2|3    -4.6060     0.1027   -44.8328
3|4    -2.9527     0.1362   -21.6747
4|5    -1.6312     0.1398   -11.6711

Residual Deviance: 1955.379 
AIC: 1973.379 
Warning: NaNs produced
                             Value   Std. Error      t value       p value
currentDF[, PROTEIN]  0.1146762900 0.0428126827     2.678559  7.393974e-03
Age                   0.0147498655 0.0004044891    36.465417 3.919741e-291
Gendermale            0.5792863925 0.0031784702   182.253209  0.000000e+00
ORdate_epoch         -0.0004569398          NaN          NaN           NaN
0|1                  -7.2796426168 0.0009020458 -8070.147119  0.000000e+00
1|2                  -5.8207609043 0.0189017920  -307.947570  0.000000e+00
2|3                  -4.6060057798 0.1027374981   -44.832762  0.000000e+00
3|4                  -2.9526553462 0.1362259590   -21.674689 3.556283e-104
4|5                  -1.6312457031 0.1397677819   -11.671114  1.790632e-31

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..


for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
    #           data  =  currentDF,
    #           Hess = TRUE)
    
    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

Analysis of PCSK9.

- processing Plaque_Vulnerability_Index
filter: removed 80 rows (13%), 542 rows remaining
filter_if: no rows removed
Warning: NaNs produced
Call:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + 
    SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)

Coefficients:
                               Value Std. Error    t value
currentDF[, PROTEIN]       1.464e-01  4.671e-02  3.135e+00
Age                        1.445e-02  5.845e-03  2.471e+00
Gendermale                 6.045e-01  2.869e-03  2.107e+02
ORdate_epoch              -5.483e-04        NaN        NaN
Hypertension.compositeyes -6.123e-02  9.373e-04 -6.533e+01
DiabetesStatusDiabetes    -1.615e-01  9.172e-04 -1.761e+02
SmokerStatusEx-smoker      1.172e-02  1.705e-03  6.873e+00
SmokerStatusNever smoked   3.697e-01  8.886e-04  4.161e+02
Med.Statin.LLDyes          6.653e-02  1.403e-03  4.743e+01
Med.all.antiplateletyes    1.757e-01  1.507e-03  1.166e+02
GFR_MDRD                   1.022e-03  3.389e-03  3.015e-01
BMI                       -4.975e-02  1.645e-02 -3.025e+00
MedHx_CVDNo               -2.365e-01  2.060e-03 -1.148e+02
stenose50-70%             -4.023e-01  3.173e-04 -1.268e+03
stenose70-90%             -7.911e-01  2.693e-03 -2.938e+02
stenose90-99%             -1.157e+00  2.578e-03 -4.487e+02
stenose100% (Occlusion)   -1.592e+00  7.429e-05 -2.142e+04
stenose50-99%             -2.842e+01  2.886e-11 -9.845e+11

Intercepts:
    Value         Std. Error    t value      
0|1 -1.062130e+01  5.000000e-04 -1.972118e+04
1|2 -9.117700e+00  2.210000e-02 -4.120315e+02
2|3 -7.889500e+00  1.127000e-01 -6.999800e+01
3|4 -6.208800e+00  1.483000e-01 -4.186870e+01
4|5 -4.843200e+00  1.526000e-01 -3.173190e+01

Residual Deviance: 1681.375 
AIC: 1727.375 
Warning: NaNs produced
                                  Value   Std. Error       t value       p value
currentDF[, PROTEIN]       1.464393e-01 4.670921e-02  3.135127e+00  1.717795e-03
Age                        1.444642e-02 5.845253e-03  2.471479e+00  1.345555e-02
Gendermale                 6.044915e-01 2.869241e-03  2.106799e+02  0.000000e+00
ORdate_epoch              -5.482731e-04          NaN           NaN           NaN
Hypertension.compositeyes -6.122961e-02 9.372731e-04 -6.532739e+01  0.000000e+00
DiabetesStatusDiabetes    -1.615202e-01 9.171699e-04 -1.761071e+02  0.000000e+00
SmokerStatusEx-smoker      1.171725e-02 1.704927e-03  6.872582e+00  6.304994e-12
SmokerStatusNever smoked   3.697313e-01 8.885704e-04  4.160967e+02  0.000000e+00
Med.Statin.LLDyes          6.653033e-02 1.402579e-03  4.743430e+01  0.000000e+00
Med.all.antiplateletyes    1.757209e-01 1.506801e-03  1.166185e+02  0.000000e+00
GFR_MDRD                   1.021946e-03 3.389078e-03  3.015410e-01  7.630020e-01
BMI                       -4.974842e-02 1.644529e-02 -3.025086e+00  2.485628e-03
MedHx_CVDNo               -2.365070e-01 2.059810e-03 -1.148198e+02  0.000000e+00
stenose50-70%             -4.022679e-01 3.172541e-04 -1.267967e+03  0.000000e+00
stenose70-90%             -7.910671e-01 2.692888e-03 -2.937616e+02  0.000000e+00
stenose90-99%             -1.156769e+00 2.578021e-03 -4.487044e+02  0.000000e+00
stenose100% (Occlusion)   -1.591670e+00 7.429345e-05 -2.142410e+04  0.000000e+00
stenose50-99%             -2.841604e+01 2.886289e-11 -9.845182e+11  0.000000e+00
0|1                       -1.062128e+01 5.385723e-04 -1.972118e+04  0.000000e+00
1|2                       -9.117742e+00 2.212875e-02 -4.120315e+02  0.000000e+00
2|3                       -7.889529e+00 1.127107e-01 -6.999802e+01  0.000000e+00
3|4                       -6.208768e+00 1.482915e-01 -4.186868e+01  0.000000e+00
4|5                       -4.843179e+00 1.526280e-01 -3.173192e+01 5.639243e-221

Session information


Version:      v1.1.0
Last update:  2023-05-23
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to analyse Targets from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_

_S_

_C_

_W_

**Changes log**
* v1.1.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
* v1.0.1 Fix to the start of this notebook.
* v1.0.0 Inital version.

sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin22.4.0 (64-bit)
Running under: macOS 14.0

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /usr/local/Cellar/r/4.3.0_1/lib/R/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Amsterdam
tzcode source: internal

attached base packages:
 [1] stats4    grid      tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] sjlabelled_1.2.0                        mia_1.8.0                               MultiAssayExperiment_1.26.0             TreeSummarizedExperiment_2.8.0         
 [5] Biostrings_2.68.1                       XVector_0.40.0                          SingleCellExperiment_1.22.0             MASS_7.3-60                            
 [9] magrittr_2.0.3                          annotables_0.2.0                        EnhancedVolcano_1.18.0                  ggrepel_0.9.3                          
[13] AnnotationFilter_1.24.0                 TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 mygene_1.36.0                           org.Hs.eg.db_3.17.0                    
[17] DESeq2_1.40.1                           SummarizedExperiment_1.30.1             MatrixGenerics_1.12.0                   matrixStats_1.0.0                      
[21] GenomicFeatures_1.52.0                  AnnotationDbi_1.62.1                    Biobase_2.60.0                          GenomicRanges_1.52.0                   
[25] GenomeInfoDb_1.36.0                     IRanges_2.34.0                          S4Vectors_0.38.1                        BiocGenerics_0.46.0                    
[29] Hmisc_5.1-0                             survminer_0.4.9                         survival_3.5-5                          GGally_2.1.2                           
[33] PerformanceAnalytics_2.0.4              xts_0.13.1                              zoo_1.8-12                              ggcorrplot_0.1.4.999                   
[37] corrr_0.4.4                             reshape2_1.4.4                          bacon_1.28.0                            ellipse_0.4.5                          
[41] BiocParallel_1.34.2                     meta_6.2-1                              qqman_0.1.8                             tidylog_1.0.2                          
[45] gridExtra_2.3                           plyr_1.8.8                              rmarkdown_2.22                          patchwork_1.1.2.9000                   
[49] labelled_2.11.0                         sjPlot_2.8.14                           UpSetR_1.4.0                            ggpubr_0.6.0                           
[53] forestplot_3.1.1                        abind_1.4-5                             checkmate_2.2.0                         pheatmap_1.0.12                        
[57] devtools_2.4.5                          usethis_2.2.0                           BlandAltmanLeh_0.3.1                    tableone_0.13.2                        
[61] openxlsx_4.2.5.2                        haven_2.5.2                             eeptools_1.2.5                          DT_0.28                                
[65] knitr_1.43                              lubridate_1.9.2                         forcats_1.0.0                           stringr_1.5.0                          
[69] purrr_1.0.1                             tibble_3.2.1                            ggplot2_3.4.2                           tidyverse_2.0.0                        
[73] data.table_1.14.8                       naniar_1.0.0                            tidyr_1.3.0                             dplyr_1.1.2                            
[77] optparse_1.7.3                          readr_2.1.4                             pander_0.6.5                            R.utils_2.12.2                         
[81] R.oo_1.25.0                             R.methodsS3_1.8.2                       worcs_0.1.10                            credentials_1.3.2                      

loaded via a namespace (and not attached):
  [1] igraph_1.4.3                ica_1.0-3                   plotly_4.10.2               Formula_1.2-5               scater_1.28.0               zlibbioc_1.46.0            
  [7] gert_1.9.2                  tidyselect_1.2.0            bit_4.0.5                   lattice_0.21-8              rjson_0.2.21                blob_1.2.4                 
 [13] urlchecker_1.0.1            S4Arrays_1.0.4              parallel_4.3.0              png_0.1-8                   tinytex_0.45                cli_3.6.1                  
 [19] bayestestR_0.13.1           ProtGenerics_1.32.0         askpass_1.1                 sjstats_0.18.2              openssl_2.0.6               goftest_1.2-3              
 [25] textshaping_0.3.6           BiocIO_1.10.0               BiocNeighbors_1.18.0        uwot_0.1.14                 curl_5.0.0                  tidytree_0.4.2             
 [31] mime_0.12                   evaluate_0.21               gsubfn_0.7                  leiden_0.4.3                stringi_1.7.12              backports_1.4.1            
 [37] XML_3.99-0.14               httpuv_1.6.11               rappdirs_0.3.3              splines_4.3.0               getopt_1.20.3               KMsurv_0.1-5               
 [43] ggbeeswarm_0.7.2            sctransform_0.3.5           sessioninfo_1.2.2           DBI_1.1.3                   jquerylib_0.1.4             withr_2.5.0                
 [49] systemfonts_1.0.4           class_7.3-22                lmtest_0.9-40               rtracklayer_1.60.0          htmlwidgets_1.6.2           fs_1.6.2                   
 [55] biomaRt_2.56.0              labeling_0.4.2              gh_1.4.0                    ranger_0.15.1               reticulate_1.29             decontam_1.20.0            
 [61] timechange_0.2.0            fansi_1.0.4                 calibrate_1.7.7             vegan_2.6-4                 irlba_2.3.5.1               commonmark_1.9.0           
 [67] ellipsis_0.3.2              lazyeval_0.2.2              yaml_2.3.7                  scattermore_1.1             crayon_1.5.2                RcppAnnoy_0.0.20           
 [73] RColorBrewer_1.1-3          progressr_0.13.0            later_1.3.1                 ggridges_0.5.4              codetools_0.2-19            base64enc_0.1-3            
 [79] profvis_0.3.8               Seurat_4.3.0                KEGGREST_1.40.0             Rtsne_0.16                  estimability_1.4.1          Rsamtools_2.16.0           
 [85] filelock_1.0.2              rticles_0.25                sqldf_0.4-11                foreign_0.8-84              pkgconfig_2.0.3             xml2_1.3.4                 
 [91] mathjaxr_1.6-0              GenomicAlignments_1.36.0    ape_5.7-1                   spatstat.sparse_3.0-1       viridisLite_0.4.2           performance_0.10.4         
 [97] xtable_1.8-4                car_3.1-2                   httr_1.4.6                  globals_0.16.2              sys_3.4.2                   SeuratObject_4.1.3         
[103] pkgbuild_1.4.0              beeswarm_0.4.0              htmlTable_2.4.1             broom_1.0.4                 nlme_3.1-162                dbplyr_2.3.2               
[109] survMisc_0.5.6              crosstalk_1.2.0             ggeffects_1.2.2             lme4_1.1-33                 digest_0.6.31               permute_0.9-7              
[115] numDeriv_2016.8-1.1         Matrix_1.5-4.1              farver_2.1.1                tzdb_0.4.0                  viridis_0.6.3               yulab.utils_0.0.6          
[121] DirichletMultinomial_1.42.0 rpart_4.1.19                glue_1.6.2                  cachem_1.0.8                BiocFileCache_2.8.0         polyclip_1.10-4            
[127] generics_0.1.3              visdat_0.6.0                CompQuadForm_1.4.3          mvtnorm_1.2-1               proto_1.0.0                 survey_4.2-1               
[133] parallelly_1.36.0           ggtext_0.1.2                pkgload_1.3.2               arm_1.13-1                  ragg_1.2.5                  ScaledMatrix_1.8.1         
[139] carData_3.0-5               minqa_1.2.5                 pbapply_1.7-0               vroom_1.6.3                 utf8_1.2.3                  mitools_2.4                
[145] sjmisc_2.8.9                ggsignif_0.6.4              shiny_1.7.4                 GenomeInfoDbData_1.2.10     clisymbols_1.2.0            RCurl_1.98-1.12            
[151] memoise_2.0.1               scales_1.2.1                future_1.32.0               reshape_0.8.9               RANN_2.6.1                  renv_0.17.3                
[157] km.ci_0.5-6                 spatstat.data_3.0-1         rstudioapi_0.14             cluster_2.1.4               spatstat.utils_3.0-3        hms_1.1.3                  
[163] fitdistrplus_1.1-11         munsell_0.5.0               cowplot_1.1.1               colorspace_2.1-0            rlang_1.1.1                 quadprog_1.5-8             
[169] sparseMatrixStats_1.12.0    DelayedMatrixStats_1.22.0   scuttle_1.10.1              mgcv_1.8-42                 xfun_0.39                   prereg_0.6.0               
[175] coda_0.19-4                 e1071_1.7-13                TH.data_1.1-2               metafor_4.2-0               modelr_0.1.11               remotes_2.4.2              
[181] emmeans_1.8.6               treeio_1.24.1               ggsci_3.0.0                 DECIPHER_2.28.0             bitops_1.0-7                ps_1.7.5                   
[187] promises_1.2.0.1            RSQLite_2.3.1               sandwich_3.0-2              DelayedArray_0.26.3         proxy_0.4-27                compiler_4.3.0             
[193] prettyunits_1.1.1           beachmat_2.16.0             boot_1.3-28.1               metadat_1.2-0               listenv_0.9.0               Rcpp_1.0.10                
[199] BiocSingular_1.16.0         tensor_1.5                  progress_1.2.2              gridtext_0.1.5              insight_0.19.2              spatstat.random_3.1-5      
[205] R6_2.5.1                    fastmap_1.1.1               multcomp_1.4-23             rstatix_0.7.2               vipor_0.4.5                 ROCR_1.0-11                
[211] rsvd_1.0.5                  vcd_1.4-11                  nnet_7.3-19                 gtable_0.3.3                KernSmooth_2.23-21          miniUI_0.1.1.1             
[217] deldir_1.0-9                htmltools_0.5.5             bit64_4.0.5                 spatstat.explore_3.2-1      lifecycle_1.0.3             zip_2.3.0                  
[223] processx_3.8.1              nloptr_2.0.3                callr_3.7.3                 restfulr_0.0.15             sass_0.4.6                  vctrs_0.6.2                
[229] spatstat.geom_3.2-1         sp_1.6-1                    future.apply_1.11.0         bslib_0.4.2                 pillar_1.9.0                locfit_1.5-9.7             
[235] jsonlite_1.8.5              markdown_1.7                chron_2.3-61               

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
© 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | vanderlaan.science. |
---
title: "Additional Figures"
author: '[Sander W. van der Laan, PhD](https://vanderlaan.science) | s.w.vanderlaan@gmail.com'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
subtitle: Accompanying 'AE_TEMPLATE'
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---

# General Setup

# General Setup
```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```

```{r Source functions}
source("scripts/functions.R")
```

```{r loading_packages, message=FALSE, warning=FALSE}
source("scripts/pack02.packages.R")

```

```{r Setting: Colors}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

source("scripts/colors.R")

```

```{r setup_notebook, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```


# Background

This notebook contains additional figures of the project.


# Loading data

```{r Loading project data}
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20230614.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))


```


# Fix some variables

We need to get the 'conventional unit' versions of cholesterols.

```{r}
AERNASE.clin <- merge(AERNASE.clin.targets, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
```


# Additional figures

## Age and sex
We want to create per-age-group figures median ± interquartile range. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by sex.
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).


```{r per Sex}

# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ Gender,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("Gender"),
                  y = "PCSK9", 
                  xlab = "gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Gender.pdf"), plot = last_plot())


```

```{r AgeGroups}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin <- AERNASE.clin %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin$AgeGroup, AERNASE.clin$Gender)
table(AERNASE.clin$AgeGroupSex)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

```{r per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
compare_means(PCSK9 ~ AgeGroup,  data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "PCSK9", 
                  xlab = "Age groups (years)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(PCSK9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin, 
                  x = c("AgeGroup"),
                  y = "PCSK9", 
                  xlab = "Age groups (years) per gender",
                  ylab = "PCSK9 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AgeGroup_perGender.pdf"), plot = last_plot())




```


## Hypertension & blood pressure
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by hypertension group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AERNASE.clin$SBPGroup, AERNASE.clin$Gender)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and hypertension/blood pressure group as median ± interquartile range.

### PCSK9

```{r}
detach("package:EnsDb.Hsapiens.v86", unload = TRUE)
detach("package:ensembldb", unload = TRUE)

```


```{r }
compare_means(PCSK9 ~ SBPGroup, data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.selfreport, data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.drugs, data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "PCSK9", 
                  xlab = "Hypertension medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(PCSK9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "PCSK9", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(PCSK9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "PCSK9", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "PCSK9", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())

```


## Hypercholesterolemia & LDL levels
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r LDLGroups}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AERNASE.clin$LDLGroup, AERNASE.clin$Gender)

```


```{r Fix Hypercholesterolemia, message=FALSE, warning=FALSE}
require(sjlabelled)

AERNASE.clin$risk614 <- to_factor(AERNASE.clin$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin)
AERNASE.clin[,"Hypercholesterolemia"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin)

table(AERNASE.clin$risk614, AERNASE.clin$Hypercholesterolemia)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

### PCSK9

```{r }

compare_means(PCSK9 ~ LDLGroup, data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(PCSK9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(PCSK9 ~ Hypercholesterolemia, data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(PCSK9 ~ Med.Statin.LLD, data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "PCSK9", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "PCSK9", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(PCSK9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "PCSK9", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "PCSK9", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "PCSK9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```


## Kidney function (eGFR)
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by kidney function. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AERNASE.clin$eGFRGroup, AERNASE.clin$Gender)

table(AERNASE.clin$eGFRGroup, AERNASE.clin$KDOQI)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and kidney function group as median ± interquartile range.

### PCSK9

```{r}

# Global test

compare_means(PCSK9 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR.pdf"), plot = last_plot())

compare_means(PCSK9 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ KDOQI, data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "PCSK9", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "PCSK9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI.pdf"), plot = last_plot())

compare_means(PCSK9 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "PCSK9", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ eGFRGroup,  data = AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "PCSK9", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "PCSK9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```


## BMI
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by BMI. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AERNASE.clin$BMI_US <- as_factor(AERNASE.clin$BMI_US)
# AERNASE.clin$BMI_WHO <- as_factor(AERNASE.clin$BMI_WHO)
# table(AERNASE.clin$BMI_WHO, AERNASE.clin$BMI_US)

table(AERNASE.clin$BMIGroup, AERNASE.clin$Gender)
table(AERNASE.clin$BMIGroup, AERNASE.clin$BMI_WHO)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9

```{r}

# Global test
compare_means(PCSK9 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI.pdf"), plot = last_plot())

compare_means(PCSK9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(PCSK9 ~ BMIGroup,  data = AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "PCSK9", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "PCSK9 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.BMI_byWHO.pdf"), plot = last_plot())

```


## Diabetes
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by type 2 diabetes. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9

```{r per Diabetes per Sex}

compare_means(PCSK9 ~ DiabetesStatus,  
              data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "PCSK9",
                  xlab = "Diabetes status",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes.pdf"), plot = last_plot())


compare_means(PCSK9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(DiabetesStatus)),
                  x = c("DiabetesStatus"),
                  y = "PCSK9",
                  xlab = "Diabetes status per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Diabetes_byGender.pdf"), plot = last_plot())
  

```


## Smoking
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by smoking. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9 

```{r per Smoking per Sex}

# Global test
compare_means(PCSK9 ~ SmokerStatus,  data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "PCSK9", 
                  xlab = "Smoker status",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking.pdf"), plot = last_plot())

compare_means(PCSK9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "PCSK9", 
                  xlab = "Smoker status per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Smoking_byGender.pdf"), plot = last_plot())

```


## Stenosis
We want to create figures of `r TRAIT_OF_INTEREST` levels stratified by stenosis grade. 

- Box and Whisker plot for `r TRAIT_OF_INTEREST` plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AERNASE.clin <- AERNASE.clin %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AERNASE.clin$StenoticGroup, AERNASE.clin$Gender)
table(AERNASE.clin$stenose, AERNASE.clin$StenoticGroup)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per sex and age group as median ± interquartile range.

### PCSK9

```{r per Stenosis per Sex}

# Global test
compare_means(PCSK9 ~ StenoticGroup,  data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "PCSK9", 
                  xlab = "Stenotic grade",
                  ylab = "PCSK9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis.pdf"), plot = last_plot())

compare_means(PCSK9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "PCSK9", 
                  xlab = "Stenotic grade per gender",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.Stenosis_byGender.pdf"), plot = last_plot())

```


## Symptoms
We want to create per-symptom figures. 

```{r SymptomGroups}
library(dplyr)

table(AERNASE.clin$AgeGroup, AERNASE.clin$AsymptSympt2G)
table(AERNASE.clin$Gender, AERNASE.clin$AsymptSympt2G)
table(AERNASE.clin$AsymptSympt2G)

```

Now we can draw some graphs of plaque `r TRAIT_OF_INTEREST` levels per symptom group as median ± interquartile range.

### PCSK9

```{r per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "PCSK9",
                  title = "PCSK9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "PCSK9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

compare_means(PCSK9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "AsymptSympt2G", y = "PCSK9",
                  title = "PCSK9 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "PCSK9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```


## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.targets.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target

```

Forest plots.

### PCSK9

```{r forestplots}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="PCSK9"]),
                  low = c(models_target$low95CI[models_target$Predictor=="PCSK9"]),
                  high = c(models_target$up95CI[models_target$Predictor=="PCSK9"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque PCSK9 normalized expression (1 SD increment, n = 622)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9.plaque.forest.pdf"), plot = fp)

rm(fp)
```


## Target expression vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the `r TRAIT_OF_INTEREST` plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the `r TRAIT_OF_INTEREST`` plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data

```{r}
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin)[names(AERNASE.clin) == "IL6"] <- "IL6rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin <- merge(AERNASE.clin, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

```


```{r Target vs Cytokines INRT, paged.print=TRUE}

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin <- AERNASE.clin %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AERNASE.clin <-  AERNASE.clin %>% mutate(AERNASE.clin[,var.temp] == replace(AERNASE.clin[,var.temp], AERNASE.clin[,var.temp]==0, NA))

  AERNASE.clin[,var.temp][AERNASE.clin[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AERNASE.clin[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AERNASE.clin[,var.temp])),").\n"))
  
  AERNASE.clin <- AERNASE.clin %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AERNASE.clin[,var.temp] - mean(AERNASE.clin[,var.temp], na.rm = TRUE))/sd(AERNASE.clin[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AERNASE.clin[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AERNASE.clin[,var.temp.rank] <- NULL
  names(AERNASE.clin)[names(AERNASE.clin) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r Target vs Cytokines Histograms}
proteins_of_interest_rank_target <- c("PCSK9", proteins_of_interest_rank)

proteins_of_interest_target <- c("PCSK9", proteins_of_interest)

for(PROTEIN_GENE in proteins_of_interest_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN_GENE in proteins_of_interest_rank_target){
  cat(paste0("Plotting protein ", PROTEIN_GENE, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin, PROTEIN_GENE,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN_GENE, " (normalized expression)"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `r TRAIT_OF_INTEREST` and 28 other cytokines. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r Target vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages.auto("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin, 
                          select = c(proteins_of_interest_rank_target)
                                    )

# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)

corr_biomarkers.rank <- round(cor(matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_target <- c("PCSK9 (RNA)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r Target vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 16,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of `r TRAIT_OF_INTEREST`` with other cytokines only.

### PCSK9 

```{r Target vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold

p1 <- ggpubr::ggbarplot(temp, 
                x = "CytokineY", 
                y = "SpearmanRho",
                fill = "CytokineY",               # change fill color by cyl
                # color = "white",            # Set bar border colors to white
                # palette = uithof_color,            # jco journal color palett. see ?ggpar
                xlab = "Cytokine",
                sort.val = "desc",          # Sort the value in dscending order
                sort.by.groups = FALSE,     # Don't sort inside each group
                x.text.angle = 45, # Rotate vertically x axis texts
                cex = 1.25
                )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(paste("Spearman's"~italic(rho))))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)


```

Another version - probably not good. 
```{r}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "PCSK9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           # palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           # ylab = expression(log[10]~"("~italic(p)~")-value"),
           # ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 4, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) +
  labs(y = expression(log[10]~"("~italic(p)~")-value"))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".PCSK9_vs_Cytokines.dotchart.pdf"), plot = last_plot())

rm(temp, p1)

```


## Target expression vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Cytokines - linear regression MODEL1 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## Target expression vs. vulnerability index


Here we plot the levels of inverse-rank normal transformed `r TRAIT_OF_INTEREST` plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 

```{r Fix ORyearGroup, message=FALSE, warning=FALSE}
library(sjlabelled)

AERNASE.clin$yeartemp <- as.numeric(year(AERNASE.clin$dateok))

attach(AERNASE.clin)

AERNASE.clin[,"ORyearGroup"] <- NA
AERNASE.clin$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin)

table(AERNASE.clin$ORyearGroup, AERNASE.clin$ORdate_year)
```

### Visualisations

#### PCSK9

```{r per PlaqueVulnerabilityIndex}
# Global test

compare_means(PCSK9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())




compare_means(PCSK9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

compare_means(PCSK9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin, 
                  x = "Plaque_Vulnerability_Index",
                  y = "PCSK9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "PCSK9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".PCSK9.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())

```


### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability index as a function of plaque `r TRAIT_OF_INTEREST` levels.

```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, paged.print=TRUE}
TRAITS.TARGET.RANK.extra = c("PCSK9")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, 
    #           Hess = TRUE)
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, paged.print=TRUE}

for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
    #           data  =  currentDF,
    #           Hess = TRUE)
    
    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.1.0
    Last update:  2023-05-23
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse Targets from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.1.0 Update to study database; update to bulk RNAseq data (deeper sequenced).
    * v1.0.1 Fix to the start of this notebook.
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [vanderlaan.science](https://vanderlaan.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+
